JP5297727B2 - Robot apparatus and object position / orientation estimation method - Google Patents

Description

  The present invention relates to a robot apparatus and an object position / orientation estimation method.

  When causing the robot to perform some operation on an object existing in the work environment, it is necessary to recognize the shape, position, and orientation of the target object prior to the operation. As a technique for estimating the position and orientation of an object, a technique for estimating the position and orientation of an object by matching with an object model is known.

Patent Document 1 discloses an image feature extraction device that estimates the position and orientation of an object by performing detailed collation after a range in which the object exists is obtained probabilistically from image features.
Patent 3300092

  However, the technique described in Patent Document 1 does not disclose a detailed description regarding the process of limiting the existence area of the object, and in the case where the area is limited only from the image features, the existence probability is set for the entire image. It is necessary to calculate and the calculation cost is high. Furthermore, when the background becomes complicated, the object model is likely to be applied to the background, and the existence probability of the object is expected to increase at various points in the image.

  By the way, in the conventional technique, the position and orientation of an object can be estimated by capturing a target object with a stereo camera and comparing an image edge / distance image from the stereo camera with an object model. In the normal collation method, the distance between the distance image and the object model is defined by a certain evaluation function, and the position and orientation of the object is estimated by solving the minimization problem.

  However, when collating with the object model, if the initial value is not set appropriately, the calculation will not converge and the calculation time will increase, and the solution will fall to an extreme value. There is. In particular, this problem becomes significant in an environment where objects having a complex texture are randomly present around an object having a small texture. When a robot enters a home environment and works, it is assumed that such an environment is encountered. In other words, when furniture (desk, chair, chiffon, etc.) is recognized by the robot, the furniture itself has relatively little texture, but there are objects with complex textures such as carpets, paintings, and posters around the furniture. Is easily assumed.

  In the robot self-position estimation and environment recognition, an active distance sensor such as a laser range finder (hereinafter referred to as LRF) is often used. However, there are limitations on objects that can be measured by LRF. The measurement principle of LRF is to measure the time until the irradiated light hits an object and is reflected and returned to measure the distance to the object. For example, when the color of the object is black, the irradiation light is absorbed, and conversely, when the object is a mirror, the object may be totally reflected. For this reason, in any case, it is difficult to accurately measure the distance to the object. When a robot enters a home environment and works, it is assumed that such an environment is encountered. That is, it is easily assumed that there are objects that cannot be measured by the LRF, such as black furniture, mirrors, and furniture with metallic luster.

Accordingly, the present invention effectively estimates the initial value of the target object, thereby solving the above-mentioned problem that occurs when collating with the object model, and accurately estimating the position and orientation of the object in a short calculation time. It is an object of the present invention to provide a robot apparatus and an object position / orientation estimation method.
Furthermore, the present invention provides a robot apparatus and an object position / orientation estimation method capable of improving robustness at the time of initial collation when estimating an object position / orientation using an active distance sensor such as a laser range finder. Another purpose is to provide

  A robot apparatus according to the present invention includes a distance sensor that generates a range data by measuring a distance to an object existing in a work environment, and a shape of the object by comparing the generated range data with shape data. An initial verification unit that estimates a position and orientation; a camera unit that generates image data obtained by imaging the object; a feature extraction unit that extracts features of the image data; and the shape and position of the object estimated by the initial verification unit Estimating the position and orientation of the object by solving the minimization problem using the error between the feature of the image data extracted by the feature extraction unit and the shape and position and orientation of the object as an evaluation function with the orientation as an initial value A posture estimation unit.

  As a result, the initial value of the target object is effectively estimated by the distance sensor, thereby solving the above-mentioned problems that occur when collating with the object model, and accurately estimating the position and orientation of the object with less calculation time. Can do.

  The feature extraction unit may extract a line segment from the image data generated by the camera unit by edge processing and acquire a distance image by stereo processing.

  Furthermore, the posture estimation unit sets a plurality of particles around the shape and position / posture of the object estimated by the initial collation unit, and projects the shape / position / posture of the object with respect to each particle. A comparison unit that compares a projection unit, a feature of the image data extracted by the feature extraction unit, a shape and a position and orientation of the object included in the particles set by the model projection unit, and a comparison by the comparison unit An evaluation unit that evaluates the weight of each particle according to the result, and the process for the particle may be repeated until the evaluation result by the evaluation unit converges.

  In addition, the initial verification unit, when the shape of the object is a bar shape or a side shape from the generated range data, and when the shape of the object is a bar shape, A bar-shaped initial position estimator that estimates the shape, position, and orientation of the object by collating the object with the shape data, and collating the object with the shape data when the shape of the object is a side shape Thus, a side shape initial position estimator for estimating the shape and position / posture of the object may be provided.

  Furthermore, a reflective marker that reflects the irradiation light of the distance sensor may be provided on the scanning surface of the distance sensor and on the surface of the object. The reflection marker may be provided at a corner of the object. Thereby, when estimating the position and orientation of an object using an active distance sensor such as a laser range finder, it is possible to improve robustness at the time of initial verification.

  Further, the distance sensor may generate the range data by measuring a distance from an object present in the work environment and a reflection intensity of irradiation light of the distance sensor. Furthermore, the distance sensor sets a predetermined threshold value for the reflection intensity of the irradiation light in accordance with the distance to the object present in the measured work environment, and the irradiation having the reflection intensity equal to or higher than the threshold value. The range data may be generated from light. Thereby, the robustness at the time of initial collation can be improved more.

  The object position / orientation estimation method according to the present invention generates a range data by measuring a distance from an object existing in a work environment by a distance sensor, and collates the generated range data with shape data. Estimating the initial shape and initial position and orientation of the object, generating image data of the object imaged by the camera, extracting the characteristics of the generated image data, and initializing the estimated initial shape and initial position and orientation of the object As a value, the position and orientation of the object are estimated by solving a minimization problem using an error between the feature of the extracted image data and the shape and position and orientation of the object.

  As a result, the initial value of the target object is effectively estimated by the distance sensor, thereby solving the above-mentioned problems that occur when collating with the object model, and accurately estimating the position and orientation of the object with less calculation time. Can do.

  Further, a line segment may be extracted from the generated image data by edge processing and a distance image may be acquired by stereo processing.

  Furthermore, a plurality of particles are set around the estimated initial shape and initial position and orientation of the object, the shape and position and orientation of the object are projected on each particle, and the extracted image data features And comparing the shape and position / orientation of the object included in the set particle, evaluating the weight of each particle according to the comparison result, and until the evaluation result converges, The process may be repeated.

  Further, it is identified from the generated range data whether the shape of the object is a rod shape or a side shape, and when the shape of the object is a rod shape, the object is collated with the shape data. To estimate the shape and position / orientation of the object by collating the object with the shape data when the shape of the object is a side shape. Also good.

  Furthermore, a reflective marker that reflects the irradiation light of the distance sensor may be provided on the scanning surface of the distance sensor and on the surface of the object. The reflection marker may be provided at a corner of the object. Thereby, when estimating the position and orientation of an object using an active distance sensor such as a laser range finder, it is possible to improve robustness at the time of initial verification.

  Further, the range data may be generated by measuring a distance from an object present in the work environment and a reflection intensity of irradiation light of the distance sensor by the distance sensor. Furthermore, a predetermined threshold value is set for the reflection intensity of the irradiation light according to the distance from the object present in the measured work environment by the distance sensor, and the irradiation has a reflection intensity equal to or higher than the threshold value. The range data may be generated from light. Thereby, the robustness at the time of initial collation can be improved more.

According to the present invention, it is possible to provide a robot apparatus and an object position / orientation estimation method capable of accurately estimating the position and orientation of an object with less calculation time.
Furthermore, when estimating the position and orientation of an object using an active distance sensor such as a laser range finder, a robot apparatus and an object position and orientation estimation method that can improve robustness at the time of initial verification are also provided. be able to.

Embodiment 1 of the Invention
FIG. 1 shows a main configuration of the robot 1 according to the first embodiment. The robot 1 can input the information of the outside world by two sensors of the laser range finder 101 and the camera unit 104, and can estimate the position and orientation of the object based on the range data and the image data acquired by these two sensors. it can. In the estimation of the position and orientation of the object, using the initial verification result based on the acquired range data, in the commentary deserves this error between the feature and the shape data of the image data, with less computation time the position and orientation of the object, And it can estimate correctly. Hereinafter, each functional block of the robot 1 will be described with reference to FIG.

  A laser range finder 101 as a distance sensor outputs laser light to the surroundings, and outputs range data obtained by observing the reflected light to the initial verification unit 102. In other words, the laser range finder 101 measures the distance to an object existing in the work environment and generates range data. FIG. 2 shows an example of range data acquired by the laser range finder 101. In FIG. 2, the scanned data is indicated by the symbol “+”. An arrow indicates a vector up to a point set around the selected point set.

  The initial collation unit 102 collates the range data obtained by the laser range finder 107 with the stored three-dimensional shape data 103, and estimates the shape and position / orientation of the object. As information indicating an object model of an object existing in the work space, three-dimensional shape data 106 which is three-dimensional geometric shape data is stored in advance. Specifically, the three-dimensional shape data viewed from the robot position and the range data are collated. Here, the error between the range data and the 3D shape data is defined by the distance or the square distance between the corresponding point of the range data and the corresponding point of the 3D shape data when the range data and the 3D shape data are collated. . The initial collation unit 102 outputs the collation result to the posture estimation unit 106 as the initial shape and initial position / posture of the object. FIG. 3 shows an example of a conceptual diagram of the initial matching result. In FIG. 3, when an object (desk) exists in front of the arrow (robot 1), the shape and position / posture of the object are indicated by a plurality of line segments. The details of the initial matching process performed by the initial matching unit 102 will be described later.

  The camera unit 104 includes two cameras 104a and 104b. Each of the cameras 104 a and 104 b includes an image sensor such as a CCD image sensor or a CMOS image sensor, and outputs digital image data obtained by imaging the outside world to the feature extraction unit 105. That is, the camera unit 104 generates image data obtained by capturing an object. FIG. 4 shows an example of image data acquired by the camera unit 104. FIG. 4 shows an example in which a desk and a chair exist in front of the robot 1.

  The feature extraction unit 105 extracts the features of the image data obtained by the camera unit 104. The feature extraction unit 105 extracts a line segment as the feature of the image data, for example, by edge processing. Moreover, a distance image is acquired by stereo processing. The feature extraction unit 105 outputs the extracted line segment image data and distance image to the posture estimation unit 106. FIG. 5 shows an example of an image edge including a line segment acquired by edge processing. In FIG. 5, the edges of the object are indicated by white line segments. FIG. 6 shows an example of a distance image (stereo data) acquired by stereo processing. In FIG. 6, the distance is indicated by the shading of the image.

The posture estimation unit 106 estimates the position and posture of the object based on the initial collation result obtained by the initial collation unit 102 and the line segment image data and distance image obtained by the feature extraction unit 105. Specifically, as an initial value using the initial matching result to estimate, in the image data characteristic (line segment of the image data and the distance image), and this to evaluate the error between the three-dimensional shape data 103, the object of the Estimate the position and orientation. FIG. 7 shows an example of the output of the estimated position and orientation of the object. FIG. 7 shows the position and orientation of the object estimated by the line segment. In FIG. 7, a line segment indicating the estimation result is displayed superimposed on the image data shown in FIG. Details of the posture estimation processing by the posture estimation unit 106 will be described later.

  Subsequently, in the following, first, the details of the initial matching process by the initial matching unit 102 will be described with reference to FIG. FIG. 8 is a flowchart showing a procedure of initial collation processing performed by the robot 1.

  First, the initial verification unit 102 acquires distance data (range data) to an object existing in a plane by plane scanning of the distance sensor (laser range finder 101) (S101).

  Next, the initial matching unit 102 classifies the distance data acquired from the distance sensor into two classes, a bar-shaped class and a side-shaped class, using a discriminator (s102). Here, the bar-shaped class includes, for example, shapes such as desks and chair legs. The side shape class includes shapes such as a chiffon and a refrigerator, for example. The classification by the classifier may be performed using a known method. For example, as a classification method, first, the acquired distance data is projected on a grid map, a point group indicating the projected distance data is regarded as a binary image, and a binary image indicating the position information of the object is generated. Then, a labeling process is executed based on the connection information in the generated binary image. The labeling process refers to a process of combining connected areas into one for the generated binary image. By performing the labeling process, the data group obtained by the distance sensor can be grouped into a plurality of groups. By calculating the roundness of the area divided in this way (that is, the grouped point group), it is possible to distinguish the bar shape from the other shapes. In general, since a distance sensor such as the laser range finder 101 measures the distance by rotating the projector, the sequence of the point sequence data obtained by the distance measurement has regularity. Therefore, there is a method in which the distance between adjacent distance measuring points is observed, point sequence data is grouped based on whether or not this distance is a predetermined threshold or more, and the linearity of the points within each group is determined. .

  Next, the initial matching unit 102 determines whether or not the distance data is a bar-shaped class (S103). If it is a bar-shaped class, the process proceeds to S104. On the other hand, if it is not a bar-shaped class, the process proceeds to S106.

  When the distance data is a bar-shaped class, the initial collating unit 102 estimates the initial position and orientation of the bar object using the object model (S104). More specifically, the initial matching unit 102 selects one of the distance data classified into the bar shape, and further selects two distance data from the distance data of the bar shape around the selected data. Then, the initial collation unit 102 calculates the distance between the bars and the angle formed between the bars, and collates with the object model (shape data 106). The matching may be performed using a known matching method. For example, the shape information of the scanning surface of the distance sensor (the length of two sides formed by the corners of the object and the angle formed by the shape information) is added in advance to the object model, and the initial verification unit 102 calculates the measurement data (distance data) and The shape information of the object model is compared. When the object model matches the distance data, the bar that is the target of matching is excluded from the next target of matching, and the matching with the object model is repeated until there is no matching target. In the case of conforming to a large number of object models, all conforming results are recorded here. Then, the initial matching unit 102 outputs the matching result in S104 (applicable object model and object position and orientation) as an initial matching result (S105).

  On the other hand, if the distance data is not a bar-shaped class, the initial matching unit 102 determines whether the distance data is a side-shaped class (S106). If it is a side shape class, the process proceeds to S107. On the other hand, if it is not a side shape class, the process proceeds to S108. That is, for the distance data that is neither the bar shape nor the side shape, the initial collation unit 102 removes the distance data without making it a collation target with the object model (S108).

  When the distance data is a side shape class, the initial matching unit 102 estimates the initial position and orientation of the side object using the object model (S107). More specifically, the initial matching unit 102 selects one of the distance data classified into the side shape, calculates the length of the side, and matches it with the object model (shape data 106). The matching may be performed using a known matching method. For example, the shape information (length of each side of the object) of the scanning surface of the distance sensor is added in advance to the object model, and the initial matching unit 102 compares the measurement data (distance data) with the shape information of the object model. To do. If the object model matches the distance data, the side that is the target of matching is excluded from the next matching target, and matching with the object model is repeated until there are no more matching targets. In the case of conforming to a large number of object models, all conforming results are recorded here. Then, the initial matching unit 102 outputs the matching result in S107 (the adapted object model and the position and orientation of the object) as the initial matching result (S105).

  Next, the details of the process for estimating the detailed position and orientation of the object based on the initial collation result will be described with reference to FIG. FIG. 9 is a flowchart illustrating a procedure of an object posture estimation process performed by the robot 1.

  First, as described above, the feature extraction unit 105 performs edge processing on the image data obtained by the camera unit 104 and extracts line segments (S201). In addition, the feature extraction unit 105 acquires a distance image (depth map) by stereo processing (S202).

  Hereinafter, the posture estimation unit 106 estimates the position and posture of the object based on the initial collation result obtained by the initial collation unit 102 and the line segment image data and distance image obtained by the feature extraction unit 105. As a method for collating with the object model based on the characteristics of the image data, a known method can be adopted. Here, a description will be given on the assumption that collation is performed using an object position / orientation estimation method using a particle filter.

  Here, each particle is constituted by a variable indicating the position and orientation of the object. For example, in the case of an object that exists on the floor surface such as a table, the object is constituted by three variables (two degrees of freedom of plane movement and one degree of freedom of rotation around the vertical axis of the floor surface). Since the number of particles can be limited to a small amount by using the result of the initial collation, the position / orientation estimation process can be speeded up.

  First, the posture estimation unit 106 sets particles using the result of the initial matching (S203). Specifically, a plurality of particles are set in the vicinity of the initial result centered on the initial shape and initial position and orientation of the object obtained as the initial collation result.

  Next, the posture estimation unit 106 projects the object model onto each particle (S204). That is, the object model (shape data 106) is projected on each set particle.

  Next, the posture estimation unit 106 performs data comparison processing between the projected object model and the image edge / distance image for each particle (S204). That is, the posture estimation unit 106 evaluates the consistency between the object model projected on each particle and the feature extracted from the image data. More specifically, an error between the characteristics of the image data (line segment image data and distance image) and the three-dimensional shape data 103 is evaluated.

  Next, the posture estimation unit 106 performs weighting processing for each particle according to the result of the data comparison processing (S206). For example, the posture estimation unit 106 sets a larger weight for a particle with a smaller error.

  Next, the posture estimation unit 106 determines whether or not the particle variables and weights have converged (S207). If the result of the determination is that it has not converged, the particles are resampled based on the weight set in S206, and the process proceeds to S205. Then, the processes after S205 are repeated until the particles converge.

  On the other hand, if the result of the determination is that it has converged, the posture estimation unit 106 outputs the value of the particle having the largest weight as the estimated value of the shape and position / posture of the object.

  Thus, by using the estimation result of the initial position and orientation by the distance sensor for the estimation of the position and orientation of the object, it is possible to limit the search range and improve the calculation speed and the recognition accuracy. In particular, even in an environment where objects with complex textures exist randomly around an object with few textures, the initial value of the target object is effectively estimated by the distance sensor, thereby reducing the position and orientation of the object. The calculation time can be accurately estimated.

Other embodiments.
With respect to the object to be estimated for position and orientation in the first embodiment described above, a reflective marker that reflects the irradiation light of the distance sensor is provided on the scanning surface of the distance sensor and on the surface of the object. Good. Furthermore, you may make it provide a reflective marker in the corner | angular part of an object. Thereby, when estimating the position and orientation of an object using an active distance sensor such as a laser range finder, it is possible to improve robustness at the time of initial verification. For example, as shown in FIG. 10A, reflection markers 4a, 4b, 4c, and 4d are provided on the surface of the corner of the object 2. The reflection markers 4a, 4b, 4c, and 4d are provided on the scanning surface of the distance sensor (the plane on the scanning line 3 of the irradiation light of the distance sensor). FIG. 10B is a conceptual diagram showing the range data acquired by the distance sensor. By providing the reflective markers 4a, 4b, 4c, and 4d, it is possible to improve the estimation accuracy of the shape and position / orientation of the object 2. it can.

  Moreover, in addition to the distance information with the object existing in the work environment, the reflection intensity information of the irradiation light of the distance sensor may be measured from the distance sensor. Furthermore, with respect to the measurement information of the distance sensor, a predetermined threshold is set for the reflection intensity of the irradiation light according to the distance from the measured object in the work environment, and the irradiation light having a reflection intensity equal to or higher than the threshold The range data may be generated only from the above. Thereby, the robustness at the time of initial collation can be improved more.

  It should be noted that the present invention is not limited to the above-described embodiment, and various modifications can be made without departing from the gist of the present invention already described.

1 is an overall view showing a main configuration of a robot according to Embodiment 1 of the present invention. It is a figure which shows an example of the range data acquired by the distance sensor which concerns on Embodiment 1 of this invention. It is a figure which shows an example of the conceptual diagram of the initial collation result which concerns on Embodiment 1 of this invention. It is a figure which shows an example of the image data acquired by the camera part which concerns on Embodiment 1 of this invention. It is a figure which shows an example of the image edge containing the line segment acquired by the edge process which concerns on Embodiment 1 of this invention. It is a figure which shows an example of the distance image (stereo data) acquired by the stereo process which concerns on Embodiment 1 of this invention. It is a figure which shows an example of the estimation result of the position and orientation of the object which concerns on Embodiment 1 of this invention. It is a flowchart figure which shows the procedure of the initial collation process which the robot which concerns on Embodiment 1 of this invention performs. It is a flowchart figure which shows the procedure of the attitude | position estimation process of the object which the robot which concerns on Embodiment 1 of this invention performs. It is a figure for demonstrating the reflective marker provided in the object which concerns on other embodiment of this invention.

Explanation of symbols

1 robot, 2 object, 3 scanning line, 4a, 4b, 4c reflective marker,
101 laser range finder, 102 initial verification unit,
103 shape data, 104 camera unit, 104a, 104b camera,
105 feature extraction unit, 106 posture estimation unit,

Claims (12)

A distance sensor that measures the distance to an object in the work environment and generates range data;
An initial collation unit that estimates the shape and position / orientation of the object by collating the generated range data with shape data;
A camera unit for generating image data obtained by imaging the object;
A feature extraction unit for extracting features of the image data;
The shape and position and orientation of the object estimated by the initial matching section as an initial value, and posture estimation unit for estimating the position and orientation of front Symbol object,
Bei to give a,
The initial matching unit is
An identifier for identifying whether the shape of the object is a bar shape or a side shape from the generated range data;
When the shape of the object is a rod shape, a rod shape initial position estimator that estimates the position and orientation of the rod object by comparing the object with the shape data;
A side shape initial position estimator that estimates the position and orientation of the side object by collating the object with the shape data when the shape of the object is a side shape;
The posture estimation unit
A plurality of particles composed of variables indicating the position and orientation of the object centered on the position and orientation of the stick object and the position and orientation of the side object estimated by the initial verification unit are set, and the object A model projection unit that projects the shape and position and orientation;
A comparison unit that compares the feature of the image data extracted by the feature extraction unit with the shape and position / posture of the object included in the particles set by the model projection unit;
An evaluation unit that evaluates the weight of each particle according to the comparison result by the comparison unit,
The process for the particles is repeated until the evaluation result by the evaluation unit converges.
Robot apparatus. The feature extraction unit includes:
The robot apparatus according to claim 1, wherein a line segment is extracted by edge processing from the image data generated by the camera unit, and a distance image is acquired by stereo processing. It said distance is on the scanning surface of the sensor, and, on the surface of the object, claim 1 or 2 SL placing the robot and providing a reflective marker that reflects irradiation light of the distance sensor. The reflective markers to claim 3 Symbol mounting of the robot apparatus, characterized in that provided in the corner of the object. The distance sensor is
Wherein the distance between an object existing in the working environment, the distance claim 1乃Optimum 4 have Zureka 1 wherein the reflection intensity of irradiation light is measured and generates the range sensor data, wherein Robotic device. The distance sensor is
A predetermined threshold is set for the reflection intensity of the irradiation light according to the measured distance to the object in the work environment, and the range data is generated from the irradiation light having the reflection intensity equal to or higher than the threshold. 5. Symbol mounting of the robot apparatus, characterized in that. Range data is generated by measuring the distance to an object in the work environment with a distance sensor,
By comparing the generated range data with shape data, the initial shape and initial position and orientation of the object are estimated,
Generating image data of the object imaged by the camera;
Extracting features of the generated image data;
The initial shape and the initial position and orientation of the object with the estimated as an initial value, a position and orientation estimation method of an object for estimating the position and orientation of front Symbol object,
When estimating the initial shape and initial position and orientation of the object by collating the generated range data with shape data,
Identifying whether the shape of the object is a rod shape or a side shape from the generated range data;
When the shape of the object is a rod shape, the position and orientation of the rod object is estimated by comparing the object with the shape data,
When the shape of the object is a side shape, the side object position and orientation is estimated by collating the object with the shape data,
When estimating the position and orientation of the object using the estimated initial shape and initial position and orientation of the object as initial values,
A plurality of particles configured with variables indicating the position and orientation of the object centered on the estimated initial position and orientation of the stick object and the initial position and orientation of the side object are set, and the shape of the object for each particle Project the position and orientation,
Compare the characteristics of the extracted image data with the shape and position / posture of the object included in the set particles,
Evaluate the weight of each particle according to the comparison result,
Until the evaluation result converges, the processing on the particles is repeated.
Position and orientation estimation method of the object body. From the image data obtained by the generated extracts a line segment by the edge processing, the position and orientation estimation method of an object according to claim 7 Symbol mounting and acquires the range image by the stereo processing. Said distance is on the scanning surface of the sensor, and, on the surface of the object, the position and orientation estimation of the distance object according to claim 7 or 8 Symbol mounting and providing a reflective marker that reflects irradiation light sensor Method. Said reflective marker, position and orientation estimation method of an object according to claim 9 Symbol mounting, characterized in that provided in the corner of the object. By the distance sensor,
Wherein the distance between an object existing in the working environment, the distance light irradiated reflection intensity and the measurement to claims 7 to 10 have Zureka preceding claim, characterized in that to produce the range data of the sensor Object position and orientation estimation method. By the distance sensor,
A predetermined threshold is set for the reflection intensity of the irradiation light according to the measured distance to the object in the work environment, and the range data is generated from the irradiation light having the reflection intensity equal to or higher than the threshold. position and orientation estimation method of an object according to claim 11 Symbol mounting, characterized in that.