JP2012154669A - On-vehicle device and bias learning method thereof - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a technology to correctly execute a bias learning processing for solving the problem of the conventional on-vehicle devices which execute a bias learning processing at a timing when a vehicle has stopped running for getting rid of an error from a rotation angle detected by a gyro sensor, and in which if wheels are not rotating it is determined in many cases that the vehicle is at rest, the problem being that in such a conventional technology a wrong bias learning processing could be executed under a situation where a vehicle is moving while rotation of its wheels has stopped, such as a situation where a vehicle is at rest on a rotating turntable.SOLUTION: An on-vehicle device of the present invention detects stopping of running of a vehicle, and when it detects the stopping of the running of the vehicle, it estimates whether the vehicle is moving or not, and if it estimates that the vehicle is not moving, it executes a bias learning of a rotation detecting device.

Description

  The present invention relates to a technology of an in-vehicle device.

  2. Description of the Related Art Conventionally, in-vehicle devices such as navigation devices use a technique for detecting the rotation angle of a vehicle by a gyro sensor and estimating the current location and the vehicle direction. Patent Document 1 describes a technique regarding such a navigation device.

JP 2009-2826 A

  In the navigation device as described above, in order to remove an error from the rotation angle detected by the gyro sensor, it is necessary to perform the bias learning process at the timing when the vehicle stops traveling. Here, it is often determined whether the vehicle is running or stopped depending on whether the wheels are rotating. In such a technique, even if the rotation of the wheel is stopped, the situation where the vehicle is moving, for example, the situation where the vehicle is stationary on the rotating turntable, or the situation where the detection of detection of the rotation of the wheel occurs. In such a case, an erroneous bias learning process may be performed.

  An object of the present invention is to provide a technique for performing bias learning processing more accurately by a simple method.

  In order to solve the above problems, an in-vehicle device according to the present invention is an in-vehicle device provided with a rotation detection device, and includes a stop detection unit that detects a stop of travel of the vehicle, and a stop of the travel of the vehicle by the stop detection unit. When detected, vehicle movement estimation means for estimating whether or not the vehicle is moving, and bias learning means for performing bias learning of the rotation detection device when the vehicle movement estimation means estimates that the vehicle is not moving And.

  According to the bias learning method of the present invention, the in-vehicle device includes a rotation detection device, and when the stop detection step for detecting the stop of the travel of the vehicle and the stop of the travel of the vehicle in the stop detection step are detected, the vehicle moves. Performing a vehicle movement estimation step for estimating whether or not the vehicle is moving, and a bias learning step for performing bias learning of the rotation detection device when the vehicle movement estimation step estimates that the vehicle is not moving. It is characterized by that.

FIG. 1 is a schematic configuration diagram of a navigation device. FIG. 2 is a diagram illustrating the configuration of the link table. FIG. 3 is a diagram showing the mounting position of the camera. FIG. 4 is a diagram illustrating a state in which a captured image is projected onto the ground surface. FIG. 5 is a functional configuration diagram of the arithmetic processing unit. FIG. 6 is a flowchart of the bias learning process. FIG. 7 is a schematic configuration diagram of a navigation device according to the second embodiment. FIG. 8 is a flowchart of the bias learning process in the second embodiment. FIG. 9 is a flowchart of the bias learning process in the third embodiment. FIG. 10 is a flowchart of bias learning processing in the fourth embodiment. FIG. 11 is a flowchart of bias learning processing in the fifth embodiment. FIG. 12 is a diagram showing the mechanism of SFM in the fifth embodiment.

  Hereinafter, a navigation device 100 that is an in-vehicle device to which a first embodiment of the present invention is applied will be described with reference to the drawings.

  FIG. 1 shows a configuration diagram of the navigation device 100.

  The navigation device 100 includes an arithmetic processing unit 1, a display 2, a storage device 3, a voice input / output device 4 (including a microphone 41 as a voice input device and a speaker 42 as a voice output device), an input device 5, and a ROM. The apparatus 6 includes a vehicle speed sensor 7, a gyro sensor 8, a GPS (Global Positioning System) receiver 9, an FM multiplex broadcast receiver 10, a beacon receiver 11, and a camera 12.

  The arithmetic processing unit 1 is a central unit that performs various processes. For example, the present location is detected based on information output from various sensors 7 and 8, the GPS receiver 9, the FM multiplex broadcast receiver 10, and the like. Further, map data necessary for display is read from the storage device 3 or the ROM device 6 based on the obtained current location information.

  The arithmetic processing unit 1 develops the read map data in graphics, and displays a mark indicating the current location on the display 2 in a superimposed manner. In addition, an optimal route (or a route or stopover) that connects the departure location (current location) instructed by the user with the map data stored in the storage device 3 or the ROM device 6 or the like is used. Search for the recommended route. Further, the user is guided using the speaker 42 and the display 2.

  The arithmetic processing unit 1 performs a bias learning process for the gyro sensor 8. For example, bias learning processing is performed by generating vector information for learning bias correction of the gyro sensor 8 and using the generated vector information as information for correcting the bias.

  The arithmetic processing unit 1 of the navigation device 100 has a configuration in which each device is connected by a bus 25. The arithmetic processing unit 1 includes a CPU (Central Processing Unit) 21 that executes various processes such as numerical calculation and control of each device, and a RAM (Random Access Memory) that stores map data, arithmetic data, and the like read from the storage device 3. ) 22, a ROM (Read Only Memory) 23 for storing programs and data, and an I / F (interface) 24 for connecting various types of hardware to the arithmetic processing unit 1.

  The display 2 is a unit that displays graphics information generated by the arithmetic processing unit 1 or the like. The display 2 is configured by a liquid crystal display, an organic EL display, or the like.

  The storage device 3 is composed of at least a readable / writable storage medium such as an HDD (Hard Disk Drive) or a nonvolatile memory card.

  This storage medium stores a link table 200 that is map data (including link data of links constituting roads on a map) necessary for a normal route search device.

  FIG. 2 is a diagram showing the configuration of the link table 200. The link table 200 includes, for each mesh identification code (mesh ID) 201, which is a partitioned area on the map, link data 202 of each link constituting a road included in the mesh area.

  For each link ID 211 that is a link identifier, the link data 202 includes coordinate information 222 of two nodes (start node and end node) constituting the link, a road type 223 indicating the type of road including the link, and a link length. Link length 224 indicating the link travel time 225 stored in advance, a start connection link that is a link connected to the start node of the link, and an end connection link that is a link connected to the end node of the link A start connection link, an end connection link 226, a speed limit 227 indicating a speed limit of a road including the link, and the like are included.

  Here, by distinguishing the start node and the end node for the two nodes constituting the link, the upward direction and the downward direction of the same road are managed as different links.

  Returning to FIG. The voice input / output device 4 includes a microphone 41 as a voice input device and a speaker 42 as a voice output device. The microphone 41 acquires sound outside the navigation device 100 such as a voice uttered by a user or another passenger.

  The speaker 42 outputs a message to the user generated by the arithmetic processing unit 1 as an audio signal. The microphone 41 and the speaker 42 are separately arranged at a predetermined part of the vehicle. However, it may be housed in an integral housing. The navigation device 100 can include a plurality of microphones 41 and speakers 42.

  The input device 5 is a device that receives an instruction from the user through an operation by the user. The input device 5 includes a touch panel 51, a dial switch 52, and other hardware switches (not shown) such as scroll keys and scale change keys.

  The touch panel 51 is mounted on the display surface side of the display 2 and can see through the display screen. The touch panel 51 specifies a touch position corresponding to the XY coordinates of the image displayed on the display 2, converts the touch position into coordinates, and outputs the coordinate. The touch panel 51 includes a pressure-sensitive or electrostatic input detection element.

  The dial switch 52 is configured to be rotatable clockwise and counterclockwise, generates a pulse signal for every rotation of a predetermined angle, and outputs the pulse signal to the arithmetic processing unit 1. The arithmetic processing unit 1 obtains the rotation angle from the number of pulse signals.

  The ROM device 6 includes at least a readable storage medium such as a ROM (Read Only Memory) such as a CD-ROM or a DVD-ROM, or an IC (Integrated Circuit) card. In this storage medium, for example, moving image data, audio data, and the like are stored.

  The vehicle speed sensor 7, the gyro sensor 8, and the GPS receiver 9 are used by the navigation device 100 to detect the current location (own vehicle position).

  The vehicle speed sensor 7 is a sensor that outputs a value used to calculate the vehicle speed.

  The gyro sensor 8 is composed of an optical fiber gyro, a vibration gyro, or the like, and detects an angular velocity due to the rotation of the moving body.

  The GPS receiver 9 receives a signal from a GPS satellite and measures the distance between the mobile body and the GPS satellite and the rate of change of the distance with respect to three or more satellites to thereby determine the current location, travel speed, and travel of the mobile body It measures the direction.

  The FM multiplex broadcast receiver 10 receives an FM multiplex broadcast signal transmitted from an FM multiplex broadcast station. FM multiplex broadcasting includes VICS (Vehicle Information Communication System: Registered Trademark) information, current traffic information, regulatory information, SA / PA (service area / parking area) information, parking information, weather information, and FM multiplex general information. As text information provided by radio stations.

  The beacon receiving device 11 receives rough current traffic information such as VICS information, regulation information, SA / PA (service area / parking area) information, parking lot information, weather information, emergency alerts, and the like. For example, it is a receiving device such as an optical beacon that communicates by light and a radio beacon that communicates by radio waves.

  As shown in FIG. 3, the camera 12 is attached so as to be able to take a picture of the road surface facing slightly downward in front of the vehicle 300. The camera 12 photographs the ground surface in front of the vehicle using an image sensor such as a CCD (Charge Coupled Device) or a CMOS (Complementary Metal Oxide Semiconductor) image sensor. In addition, the camera 12 delivers the captured video to the arithmetic processing unit 1.

  FIG. 4 is a diagram for explaining a method of generating a ground projection image using a video photographed by the camera 12 of FIG. The main control unit 101 obtains the position of the viewpoint P of the camera 12 (coordinate position in a three-dimensional space with a predetermined position in the vehicle as the origin) and the shooting direction (gaze direction) K. Then, the main control unit 101 projects the captured image 510 on the ground surface 520 from the position of the viewpoint P of the camera 12 in the imaging direction K, and generates a ground projection image 530. Note that the shooting direction K intersects the center of the shot image 510 perpendicularly. Further, the distance from the viewpoint P of the camera 12 to the captured image 510 is determined in advance. The ground projection image 530 generated in this way is an image that looks like a bird's-eye view of the vehicle periphery from above the vehicle.

  FIG. 5 is a functional block diagram of the arithmetic processing unit 1. As illustrated, the arithmetic processing unit 1 includes a main control unit 101, an input receiving unit 102, an output processing unit 103, a stop determination unit 104, a bias learning processing unit 105, and a timer unit 106.

  The main control unit 101 is a central functional unit that performs various processes, and controls other processing units according to the processing content. In addition, information on various sensors, the GPS receiver 9 and the like is acquired, and a map matching process is performed to identify the current location. In addition, the travel history is stored in the storage device 3 for each link by associating the travel date and time with the position as needed. Further, the current time is output in response to a request from each processing unit. Further, an optimum route (recommended route) connecting the starting point (current location) and the destination instructed by the user is searched, and the user is guided using the speaker 42 and the display 2 so as not to deviate from the recommended route.

  The input receiving unit 102 receives an instruction from the user input via the input device 5 or the microphone 41, and controls each unit of the arithmetic processing unit 1 so as to execute processing corresponding to the requested content. For example, when the user requests a search for a recommended route, the output processing unit 103 is requested to display a map on the display 2 in order to set a destination.

  The output processing unit 103 receives screen information to be displayed, converts it into a signal for drawing on the display 2, and instructs the display 2 to draw.

  The stop determination unit 104 receives vehicle speed pulse information from the vehicle speed sensor 7 or information indicating the state of the handbrake obtained via the main control unit 101, and determines whether or not the vehicle has stopped traveling. Specifically, for example, the stop determination unit 104 determines that the vehicle is stopped when the generation of vehicle speed pulse information is not detected for a certain period (for example, 3 seconds). Alternatively, the stop determination unit 104 determines that the vehicle is stopped when detecting that the hand brake is applied. Alternatively, the stop determination unit 104 determines that the vehicle is stopped when the position of the feature point of the object included in the plurality of continuous images captured by the camera 12 does not change.

  The bias learning processing unit 105 performs processing for learning (setting) the bias of the gyro sensor 8 when the vehicle is stopped.

  The time measuring unit 106 measures the passage of time from the time when the time measurement is instructed to the time when the time measurement is instructed. The time measuring unit 106 responds with information specifying the elapsed time in response to the request while measuring the passage of time.

  The functional units of the arithmetic processing unit 1 described above, that is, the main control unit 101, the input reception unit 102, the output processing unit 103, the stop determination unit 104, the bias learning processing unit 105, and the time measuring unit 106, the CPU 21 reads a predetermined program. Constructed by executing. Therefore, the RAM 22 stores a program for realizing the processing of each functional unit.

  In addition, each above-mentioned component is classified according to the main processing content, in order to make an understanding easy the structure of the navigation apparatus 100. FIG. Therefore, the present invention is not limited by the way of classifying the components and their names. The configuration of the navigation device 100 can be classified into more components depending on the processing content. Moreover, it can also classify | categorize so that one component may perform more processes.

  Each functional unit may be constructed by hardware (ASIC, GPU, etc.). Further, the processing of each functional unit may be executed by one hardware or may be executed by a plurality of hardware.

[Description of operation]
Next, the operation of the navigation device 100 will be described.

  FIG. 6 is a flowchart of bias learning processing for performing bias learning of the gyro sensor when the vehicle stops. This flow is periodically started at a predetermined interval (for example, every 3 seconds) while the navigation device 100 is operating.

  First, the main control unit 101 detects vehicle speed pulse information via the vehicle speed sensor 7 (step S001).

  The vehicle speed pulse information is pulse information that is generated when the vehicle speed sensor 7 detects the rotation of a wheel. Therefore, when the wheel is not rotating, that is, when the vehicle is not traveling, or when the vehicle speed sensor 7 cannot detect the rotation of the wheel, the main control unit 101 can detect the vehicle speed pulse information. Can not.

  Next, the main control unit 101 determines whether or not the vehicle is traveling (autonomous movement). Here, for example, it is determined whether or not the vehicle speed pulse is not detected for a predetermined time (for example, 2 seconds). Specifically, the main control unit 101 determines that the vehicle speed pulse is not detected (the vehicle is not running) when no pulse is detected within a predetermined time in step S001. .

  When a vehicle speed pulse is detected within a predetermined time (“No” in step S002), the timekeeping unit 106 stops the timekeeping when the timekeeping process is operating, resets the timekeeping, and performs a bias learning process. Is terminated (step S003). That is, since the navigation apparatus 100 determines that the vehicle is traveling, the bias learning process is terminated without performing the bias learning.

  When the vehicle speed pulse is not detected within the predetermined time (“Yes” in step S002), the stop determination unit 104 is connected to the camera 12 via the main control unit 101 at predetermined intervals (for example, 1/10 second ( Instruct to shoot (at 100 millisecond intervals) and detect the movement of the feature points of the captured image (step S004). Specifically, the stop determination unit 104 detects an edge portion or the like included in a captured image as one or a plurality of feature points, and detects a feature point that has moved between the captured images.

  Then, the stop determination unit 104 determines whether or not the movement of the vehicle (non-autonomous movement, for example, conveyance by a rotating vehicle installation table (turn table)) is estimated (step S005). Specifically, the stop determination unit 104 estimates that the vehicle has moved when the positions of the feature points occupying a predetermined ratio (for example, 80%) of the feature points move on the image, Otherwise, it is estimated that the vehicle is not moving.

  When the (non-autonomous) movement of the vehicle is estimated (“Yes” in step S005), the timer unit 106 performs the process of step S003 described above and ends the bias learning process.

  When the (non-autonomous) movement of the vehicle is not estimated (“No” in step S005), the timer unit 106 determines whether or not the timing process is in operation (step S006). When the timing process is in operation (“Yes” in step S006), the timing unit 106 executes step S008 described later. If the timing process is not in operation (“No” in step S006), the timing unit 106 starts timing (step S007) and executes step S008 described later.

  Then, the timer 106 acquires the elapsed time from the start of timing and determines whether or not a threshold (for example, 15 seconds) has elapsed (step S008).

  If the elapsed time from the start of timing is equal to or greater than the threshold (“Yes” in step S008), the bias learning processing unit 105 performs bias learning (step S009), and ends the bias learning process after the completion of the execution. During bias learning, the bias learning processing unit 105 prevents double activation by preventing further start of the bias learning processing.

  If the elapsed time from the start of timing is not equal to or greater than the threshold (“No” in step S008), the bias learning processing unit 105 ends the bias learning processing without performing bias learning. The above is the processing content of the bias learning processing.

  By performing the bias learning process, the navigation device 100 can perform the bias learning process only when the vehicle has stopped traveling for a certain period of time and the vehicle has not moved.

  The embodiment of the present invention has been described above.

  According to one embodiment of the present invention, the navigation apparatus 100 can perform bias learning by appropriately determining a state in which the vehicle is not traveling. More specifically, the navigation device 100 can not perform bias learning when the vehicle is moving even in a state where the vehicle is not traveling with the wheels rotated. In other words, when the vehicle receives rotational force from a turntable, etc., or when the vehicle is mounted on a ship or the like and the ship shakes, it is possible to avoid erroneously learning the bias of the rotational force or ship shake. .

  The present invention is not limited to the first embodiment. The first embodiment described above can be variously modified within the scope of the technical idea of the present invention. For example, in the bias learning process of the first embodiment, the stop of the vehicle is determined using the camera 12, but the present invention is not limited to this, and the stop of the vehicle is determined using an ultrasonic sensor. Also good. Specifically, as a second embodiment, as shown in FIG. 7, it is possible to modify the bias learning process as shown in FIG. is there.

  FIG. 7 is a diagram illustrating a configuration of the navigation device 100 according to the second embodiment. Basically, the navigation apparatus 100 has the same configuration as that of the navigation device 100 in the first embodiment, but in the second embodiment, the arithmetic processing unit 1 uses ultrasonic waves to locate the nearest in a predetermined direction. The difference is that an ultrasonic sensor 13 that measures the distance to the object by detecting the reflected wave is provided.

  FIG. 8 is a diagram showing a processing flow of bias learning processing in the second embodiment. In the bias learning process in the second embodiment, instead of the processes corresponding to steps S004 and S005 in the first embodiment, processes in steps S104 and S105 described later are performed.

  In step S104, the stop determination unit 101 measures the distance from the object at a predetermined interval (for example, at an interval of 1/10 second (100 milliseconds)) with respect to the ultrasonic sensor 13 via the main control unit 101. And the distance to the measured object is detected (step S104). Specifically, the stop determination unit 101 identifies the distance to the nearest object and detects a change in the identified distance.

  Then, the stop determination unit 104 estimates that the vehicle has moved when the distance has changed (for example, when the distance becomes shorter), and otherwise estimates that the vehicle has not moved. To do. And the stop determination part 104 determines whether the movement of a vehicle is estimated (step S105).

  The above is the content of the bias learning process in the second embodiment. In this way, in the second embodiment, the navigation device 100 can detect the stop of the vehicle even in dark conditions such as at night or in poor visibility conditions such as rainy weather.

  In the bias learning process of the first embodiment, the camera 12 captures the surroundings of the vehicle and determines the stop of the vehicle based on changes in surrounding objects. However, the present invention is not limited to this. The stop of the vehicle may be determined based on the rotation state of the wheel of the own vehicle that is captured in the captured image. Specifically, as the third embodiment, the bias learning process can be modified as shown in FIG.

  FIG. 9 is a diagram showing a processing flow of bias learning processing in the third embodiment. In the bias learning process in the third embodiment, instead of the processes corresponding to steps S001 and S002 in the first embodiment, processes in steps S201 and S202 described later are performed.

  In step S201, the main control unit 101 detects the rotation of the wheel using the image captured by the camera 12 (step S201). For the rotation of the wheel, the stop determination unit 104 instructs the camera 12 to photograph the wheel at a predetermined interval (for example, at an interval of 1/10 second (100 milliseconds)) via the main control unit 101. The detection is performed by detecting the movement of the feature point of the captured image. That is, the stop determination unit 104 determines that the vehicle has not stopped when the movement of the feature point of the wheel is detected by comparing a plurality of captured images.

  Next, the main control unit 101 determines whether or not the movement of the feature point of the wheel is not detected for a predetermined time (for example, 2 seconds) (step S202). Specifically, the main control unit 101 determines that the rotation of the wheel is not detected when the movement of the feature point of the wheel has not been detected within a predetermined time in step S201.

  The above is the content of the bias learning process in the third embodiment. By doing in this way, in 3rd embodiment, the navigation apparatus 100 becomes possible [detecting the low speed rotation of a wheel]. That is, since it is possible to detect a minute rotation of the wheel rather than detecting the vehicle speed pulse by the vehicle speed sensor 7, it is easy to detect the traveling of the vehicle at a very low speed.

  In the bias learning process of the first embodiment, the camera 12 captures the surroundings of the vehicle and determines the stop of the vehicle based on changes in surrounding objects. However, the present invention is not limited to this. The change in the position of the camera 12 itself may be estimated from the change in the image captured in the captured image, the change in the position of the camera may be detected as the movement of the vehicle, and the stop of the vehicle may be determined. Specifically, as the fourth embodiment, the bias learning process can be modified as shown in FIG.

  FIG. 10 is a diagram illustrating a processing flow of bias learning processing according to the fourth embodiment. In the bias learning process in the fourth embodiment, instead of the processes corresponding to steps S004 and S005 in the first embodiment, processes in steps S304 and S305 described later are performed.

  In step S304, the stop determination unit 104 instructs the camera 12 to shoot at a predetermined interval (for example, at an interval of 1/10 second (100 milliseconds)) via the main control unit 101, and shoots. Based on the change in the position of the feature point of the image, the position and orientation of the camera 12 are estimated from the image information using a method such as SFM (Structure From Motion), and the change in the position and direction of the camera 12 is estimated. (Step S304). The SFM will be described later with reference to FIG.

  Then, the stop determination unit 104 estimates that the vehicle has moved when the estimated position and / or direction of the camera 12 changes, and otherwise estimates that the vehicle has not moved. To do. And the stop determination part 104 determines whether the movement of a vehicle is estimated (step S305).

  The above is the content of the bias learning process in the fourth embodiment. In this manner, in the fourth embodiment, the navigation device 100 accurately determines whether or not the vehicle has moved even when the subject of the camera moves (for example, when a pedestrian or the like moves). It becomes possible to detect.

  Alternatively, in the bias learning process of the first embodiment, the camera 12 captures the surroundings of the vehicle and determines the stop of the vehicle based on changes in surrounding objects. However, the present invention is not limited to this. The change in the position and direction of the camera 12 itself is estimated from the change in the image, the coordinates of the predetermined subject in the coordinate system with the camera position as the origin (reference) are estimated, and the position of the predetermined subject is determined. If there is a change, it may be determined that the vehicle has stopped by estimating that the vehicle has moved. Specifically, as the fifth embodiment, the bias learning process can be modified as shown in FIG.

  FIG. 11 is a diagram illustrating a processing flow of bias learning processing in the fifth embodiment. In the bias learning process in the fifth embodiment, instead of the processes corresponding to steps S004 and S005 in the first embodiment, processes in steps S404 and S405 described later are performed.

  In step S404, the stop determination unit 104 instructs the camera 12 to shoot at a predetermined interval (for example, at an interval of 1/10 second (100 milliseconds)) via the main control unit 101, and shoots. Based on the change in the position of the feature point of the image, the position and orientation of the camera 12 are estimated from the image information using a method such as SFM (Structure From Motion), and the change in the position and direction of the camera 12 is estimated. . Then, the stop determination unit 104 estimates a coordinate value in a coordinate system using the estimated position of the camera 12 as an origin (reference) for a predetermined subject (for example, a subject having the highest luminance) included in the image information, and the subject Is detected (step S404). The SFM will be described later with reference to FIG.

  Then, the stop determination unit 104 estimates that the vehicle has moved when the estimated coordinate value of the subject has changed, and otherwise estimates that the vehicle has not moved. Then, stop determination unit 104 determines whether or not the movement of the vehicle is estimated (step S405).

  The above is the content of the bias learning process in the fifth embodiment. In this way, in the fifth embodiment, the navigation device 100 can accurately calculate the coordinates of the subject, and thus can more accurately detect whether or not the vehicle has moved. It becomes possible.

  Next, the outline of the SFM process used in the bias learning process in the fourth embodiment and the fifth embodiment will be described with reference to FIG. FIG. 12A is a model diagram showing the relationship between a camera and a subject for explaining the principle of SFM. FIG. 12B is a diagram illustrating an example of the captured image in FIG.

  In FIG. 12A, a camera 400, a subject P1 (410), and a subject P2 (420) are shown. The position of the camera 400 changes from position F1 to position F2 and from position F2 to position F3 with the passage of time. Of the motion components when the camera 400 moves from the position F1 to the position F2, the rotation matrix indicating rotation is R1, and the translation vector indicating the movement of the camera position is T1. Of the motion components when the camera 400 moves from the position F2 to the position F3, a rotation matrix indicating rotation is R2, and a translation vector indicating movement of the camera position is T2. Note that the rotation matrix is information for specifying a change in the shooting direction of the camera 400 by a conversion matrix on the three-dimensional coordinates, and the translation vector is for specifying whether the position of the camera 400 is moved on the three-dimensional coordinates. Information. For example, when the rotation matrix R1 is [0, 0, 0], it indicates that the camera 400 is not rotating, and when the translation vector T1 is [0, 0, 0], the camera 400 indicates that it has not moved. Further, the positions of the subject P1 (410) and the subject P2 (420) are not changed.

  In FIG. 12B, the image (452) of the subject P1 recorded in the image SCR1 (451) photographed by the camera 400 at the position F1 and the image SCR2 (461) photographed by the camera 400 at the position F2. A video image (462) of the subject P1 is displayed. Here, in the image SCR1 (451), the image (452) of the subject P1 is recorded near the center thereof, and in the image SCR2 (461), the image (462) of the subject P1 is recorded on the image SCR1 (451). Compared to the left.

  Here, an overview of the SFM process will be described. First, the stop determination unit 104 identifies feature points having features in edges and luminance values, such as corners and points included in the captured image (SCR1), and tracks the feature points on time-series image data. . Next, the stop determination unit 104 estimates motion information between F1 and F2 of the camera 400 using the principle of weakly calibrated stereo between time-series image data. Here, a rotation matrix and a translation vector are used as motion information. That is, the stop determination unit 104 specifies the rotation matrix R1 and the translation vector T1 as motion information between the position F1 and the position F2. The rotation matrix is obtained as a three-dimensional matrix [Rx, Ry, Rz], and the translation vector is obtained as a three-dimensional vector [Tx, Ty, Tz].

  In this way, the stop determination unit 104 calculates the rotation matrix R2 and the translation vector T2 when moving from the position F2 to the position F3, and sequentially obtains motion information for the subsequent movement of the camera 400. Then, the stop determination unit 104 refers to the motion information, and determines that the camera 400 is not moving when both the rotation matrix and the translation vector are zero.

  In the fifth embodiment, the stop determination unit 104 obtains the coordinates [Px, Py, Pz] of the subject P1 (410) on the xyz three-dimensional coordinates with the camera position as the origin (reference), and the camera Is compared with the coordinates [Px ′, Py ′, Pz ′] of the subject P1 (410) when the position is moved to F2. The stop determination unit 104 determines that the camera 400 is not moving when all of Px and Px ′, Py and Py ′, and Pz and Pz ′ match. This is the content of processing using SFM.

  In the first to fifth embodiments, when the stop determination unit 104 detects that there is no wheel rotation for a predetermined time by detecting a vehicle speed pulse or the like, the stop determination unit 104 uses a camera or the like to However, this is not a limitation. That is, the predetermined time may be a different value depending on the vehicle condition.

  Specifically, a predetermined time corresponding to the vehicle situation is stored in advance in the storage device 3 or the ROM 23 of the navigation device 100, and the stop determination unit 104 specifies the predetermined time according to the vehicle situation. You may make it do. For example, when the current location belongs to a road, the predetermined time may be set to a short time (for example, 5 seconds). When the current location belongs to a parking lot, the predetermined time is set to a long time (for example, 30 seconds). ). In this way, the bias learning process can be performed during a short stoppage time such as waiting for a signal. That is, by performing the bias learning process in detail, the current location specified using the detection value of the gyro sensor can be specified accurately. Also, in places where there is a low necessity for specifying the current location, such as in a parking lot, it is possible to reduce the frequency of the bias learning process and suppress the power consumption of the navigation device.

  Furthermore, you may combine all or some of each invention technique of said 1st embodiment to 5th embodiment. For example, the bias learning processing unit 105 may perform bias learning when it is detected by a plurality of detection means that there is no movement of the vehicle.

  The present invention has been described with reference to the first to fifth embodiments.

  In the above embodiment, an example in which the present invention is applied to an in-vehicle navigation device has been described. However, the present invention is not limited to an in-vehicle navigation device and can be applied to all in-vehicle devices.

DESCRIPTION OF SYMBOLS 1 ... Arithmetic processing part, 2 ... Display, 3 ... Memory | storage device, 4 ... Voice output device, 5 ... Input device, 6 ... ROM device, 7 ... Vehicle speed sensor 8 ... Gyro sensor, 9 ... GPS receiver, 10 ... FM multiplex broadcast receiver, 11 ... Beacon receiver, 12 ... Camera, 13 ... Ultrasonic sensor, 21. ..CPU, 22 ... RAM, 23 ... ROM, 24 ... I / F, 25 ... bus, 41 ... microphone, 42 ... speaker, 51 ... touch panel, 52 ..Dial switch, 100 ... navigation device, 101 ... main control unit, 102 ... input receiving unit, 103 ... output processing unit, 104 ... stop determination unit, 105 ... bias learning Processing unit 106 ... Time measuring unit 200 ... Link table

Claims (8)

An in-vehicle device provided with a rotation detection device,
Stop detection means for detecting stoppage of the vehicle,
Vehicle movement estimation means for estimating whether or not the vehicle is moving when the stop detection means detects a stop of travel of the vehicle;
Bias learning means for performing bias learning of the rotation detecting device when the vehicle movement estimating means estimates that the vehicle is not moving;
A vehicle-mounted device comprising: The in-vehicle device according to claim 1,
The vehicle movement estimation means estimates the stop of the vehicle by the image captured by the camera device that captures the surroundings of the vehicle not changing for a predetermined time.
In-vehicle device characterized by the above. The in-vehicle device according to claim 1 or 2,
The vehicle movement estimation means identifies a change in the position of the camera device based on a change in an image captured by a camera device that captures the surroundings of the vehicle, and stops the vehicle when the position of the camera device does not change for a predetermined time. Estimate
In-vehicle device characterized by the above. The in-vehicle device according to claim 3,
The vehicle movement estimation means identifies the presence or absence of rotation based on information for identifying the rotation direction of the camera device based on a change in an image captured by a camera device that captures the surroundings of the vehicle, and identifies the movement direction of the camera device The presence or absence of movement based on information is specified, and the camera device is not rotated for a predetermined time and is not moved, thereby estimating the stop of the vehicle.
In-vehicle device characterized by the above. The in-vehicle device according to any one of claims 1 to 4,
The vehicle movement estimation means identifies a change in position and rotation of the camera device based on a change in an image captured by a camera device that captures the surroundings of the vehicle, and in the coordinate system based on the position of the camera device, the image The coordinate value of a predetermined feature point included in the vehicle does not change for a predetermined time, thereby estimating the stop of the vehicle.
In-vehicle device characterized by the above. It is an in-vehicle device according to any one of claims 1 to 5,
The vehicle movement estimation means estimates the distance to the obstacle by the reflected wave of ultrasonic waves emitted around the vehicle, and estimates the stop of the vehicle by the distance not changing for a predetermined time.
In-vehicle device characterized by the above. The on-vehicle device according to any one of claims 1 to 6, further comprising:
A wheel photographing means for photographing the wheel of the vehicle;
The stop detection unit detects the stop of the vehicle when rotation of the wheel is stopped for a predetermined time in the image captured by the wheel photographing unit.
In-vehicle device characterized by the above. A bias learning method for an in-vehicle device,
The in-vehicle device is
Equipped with a rotation detector,
A stop detection step for detecting a stop of the running of the vehicle;
A vehicle movement estimation step for estimating whether or not the vehicle is moving when detecting the stop of the running of the vehicle in the stop detection step;
A bias learning step of performing bias learning of the rotation detection device when the vehicle movement estimation step estimates that the vehicle is not moving;
A bias learning method characterized by comprising:

Images