WO2022041596A1 - 一种适用于室内动态环境的视觉slam方法 - Google Patents
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- the invention belongs to the technical field of robotics and computer vision, and in particular relates to a visual SLAM method suitable for indoor dynamic environments.
- SLAM simultaneous localization and mapping
- real-time positioning and map construction or concurrent mapping and positioning.
- the robot starts to move from an unknown position in an unknown environment, and performs its own positioning according to the position estimation and map during the movement process.
- an incremental map is built on the basis of its own positioning to realize the autonomous positioning and navigation of the robot.
- the visual SLAM technology enables the subject equipped with the camera sensor to complete the establishment of the environment model and the estimation of its own motion state during the movement of the subject without the prior information of the environment.
- visual SLAM systems have been widely used in mobile robots, service robots, drones, virtual reality and augmented reality.
- the existing classic visual SLAM framework is mainly based on the assumption that the subject equipped with the camera sensor is the only moving object in the mapping environment, and the rest of the objects in the default mapping environment are always in a static state.
- Such a classic visual SLAM framework can obtain good experimental results in static scenes, but when the mapping scene contains dynamic object information, such as people walking and cars moving in the scene, the classic visual SLAM framework still uses these dynamic objects.
- the object is in a static state by default, and the feature points in the dynamic object area in the scene cannot be removed in time.
- the feature points extracted from the dynamic object are also included in the geometric constraints and graph optimization constraints, which will cause a large error in the SLAM system. .
- the purpose of the present invention is to provide a visual SLAM method suitable for indoor dynamic environment, which eliminates the adverse effects of dynamic objects in the environment on the pose estimation of the SLAM system in time.
- the technical scheme adopted in the present invention is:
- a visual SLAM method suitable for indoor dynamic environments including:
- the 3D map point dynamic probability enjoys the preferred propagation right, that is to say, the feature point x i of the current frame is successfully matched with the key frame feature point, the feature point x i of the previous frame and the 3D map point x i in the local map.
- the dynamic probability P(X i ) of the 3D map point is preferentially propagated to the dynamic probability P(X i ) of the feature point of the current frame.
- the dynamic probability of any feature point in the current frame is first affected by the dynamic probability of the key frame feature point or 3D map point that is successfully matched with it: for the successfully matched feature point, the key frame feature point or 3D map point
- the dynamic probability of map points updates the dynamic probability of feature points in the current frame through a certain weight
- the high dynamic rate feature point is composed of a feature point with a very high or very low dynamic probability.
- the high dynamic rate feature point will affect the dynamic probability of the remaining feature points in the area with a certain pixel distance as the center and a certain pixel distance as the radius.
- the dynamic probability propagation includes feature matching propagation and high-confidence feature point regional influence propagation.
- the possibility that the feature points or 3D map points represented by the dynamic probability belong to the dynamic points are equally divided into five levels from 0% to 100%, wherein: the high-confidence feature point area includes high
- the reliability feature points are low dynamic rate (0% to 20%) feature points, and high reliability feature points are high dynamic rate (80% to 100%) feature points.
- the distance r (in pixels) is selected as high probability.
- the regional influence radius of the reliability feature points For the feature points within the influence radius of the high reliability feature point region, the dynamic probability will be affected by the high reliability feature points.
- the distance of the points is inversely proportional.
- the optimization equation is to minimize the reprojection error
- the current frame pose is the optimization object to build a pose optimizer
- the feature points with high dynamic probability are set as outliers
- the current frame pose is used to minimize the reprojection error.
- the frame pose is optimized, and the feature points whose reprojection error exceeds the threshold in the current frame are removed as outliers.
- the nonlinear optimization will be performed multiple times, and the reprojection error of all points is calculated after each optimization. If the reprojection error is less than the threshold, the point is set as the interior point, otherwise the point is set as the outer point, and the outer point is set as the outer point. Do not participate in the next optimization process.
- a neural network is used to perform target detection on the key frame
- a pre-trained neural network is used to perform target detection
- the target detection network determines an object of a dynamic category
- a semantic segmentation algorithm is used to extract the object in the image.
- the mid-contour acts as a dynamic region.
- the forward propagation process of the key frame target detection network and the visual SLAM system are working nodes under two different ROS systems, and the interaction between them is realized through the topic and message mechanism under the ROS system.
- the feature points in the dynamic area are identified as dynamic points, and the remaining feature points are identified as static feature points, and all feature points of the detected key frame are updated and corresponding Dynamic probabilities of matching 3D map points.
- the dynamic object category needs to be set according to the use environment of the system, and the three object categories of people, cats and dogs are set as dynamic object categories in an indoor environment.
- S5 local optimization is performed on the poses of key frames and 3D map points by a graph optimization method, and the optimization equation is used to minimize the reprojection error equation, and the poses and maps of the remaining frames with a high degree of common vision with the current frame are used as the optimization equation.
- the point pose is the optimization object, and a graph optimizer is constructed to optimize the pose obtained by the tracking thread.
- the present invention has the following advantages compared with the prior art:
- the present invention When the present invention performs pose calculation and map construction, it effectively integrates the object category information in the environment, integrates the target detection algorithm with the traditional visual SLAM system, introduces the "dynamic probability" attribute for the feature points and map points, and promptly eliminates the attributes that belong to the environment.
- the feature points of dynamic objects have higher positioning and mapping accuracy and stronger robustness in dynamic environments.
- FIG. 2 is a schematic diagram of the division of each stage of dynamic probability in the present embodiment
- FIG. 7 is a graph of the results compared with ORB-SLAM2 in this example.
- Figure 1 A visual SLAM method suitable for indoor dynamic environments, including:
- RGB-D camera Use the RGB-D camera to obtain the color map and depth map of the environment, package them into frames, input the tracking thread, perform feature extraction and matching, and use the matching relationship to calculate the dynamic probability propagation result.
- Dynamic probability propagation consists of feature matching propagation and high-confidence feature point regional influence propagation.
- the feature matching process if the feature point x i of the current frame is successfully matched with the key frame feature point, the dynamic probability of the key frame feature point Propagated to the dynamic probability P(x i ) of the feature point of the current frame, if the feature point x i of the current frame is successfully matched with the 3D map point X i in the local map, then the dynamic probability of the 3D map point P(X i ) Propagated to the dynamic probability P(x i ) of the feature points of the current frame, and the dynamic probability of the 3D map point enjoys the preferred propagation right.
- the probability that the feature points or 3D map points represented by the dynamic probability belong to dynamic points is divided into five levels from 0% to 100%, as shown in Figure 2, where:
- the high-confidence feature point area includes high-confidence features Point low dynamic rate (0% ⁇ 20%) feature point, high reliability feature point high dynamic rate (80% ⁇ 100%) feature point, selected distance r (in pixels) as high reliability feature point
- the dynamic probability of the feature points within the influence radius of the high-confidence feature point area will be affected by the high-confidence feature point, and the influence weight is proportional to the distance between the feature point and the high-confidence feature point. inversely proportional.
- the dynamic probability of any feature point in the current frame is also affected by the dynamic probability of the feature point and its neighborhood on the matching 3D map point keyframe: please confirm if the feature content is missing;
- the dynamic probability of the feature points in the current frame is first affected by the dynamic probability of the feature points on the matching key frame and the 3D map points: the feature points with very high or very low dynamic probability affect the feature points with their center and a certain pixel distance as the radius.
- the dynamic probabilities of the remaining feature points in the region are shown in Figure 3.
- the optimization equation is to minimize the reprojection error, and the current frame pose is the optimization object to build a pose optimizer.
- the high dynamic rate feature points are set as outliers, that is, the high dynamic rate feature points do not participate in the first time.
- the reprojection error is calculated for all feature points including the outer points. If the reprojection error of the outer points (ie, the high dynamic rate feature points) is less than the set threshold , then it means that the feature point is mistaken as a dynamic point, and it is reset as an interior point to participate in the next optimization operation.
- the feature point is omitted in the process of propagation, and it is set as an outer point and does not participate in the next optimization.
- the pose optimizer performs a total of 4 optimization times. After the optimization is completed, all are removed as dynamic points, as shown in Figure 4.
- the target detection network selects the YOLO V3 network structure based on Darknet53, and the network outputs a tensor with a dimension of (G ⁇ G) ⁇ (B ⁇ 5+C) for each input color image, where G ⁇ G represents the grid division scale of the network feature map, B represents the candidate frame information corresponding to the output of each grid, and each candidate frame information contains five values of the candidate frame center coordinates, the candidate frame length, width and confidence. , C represents the dataset item classification during network training.
- the pose information and category information provided by the target detection network three categories of people, cats and dogs are selected as dynamic object categories, and the Grab-Cut semantic segmentation algorithm is used for the rectangular area of the dynamic object category, and the contour area of the dynamic object is extracted as dynamic area.
- the feature points in the dynamic area are identified as dynamic points, and the remaining feature points are identified as static feature points, and the dynamic probability of all feature points in the detected key frame and the matching map points is updated.
- the local mapping thread of ORB-SLAM2 is used to minimize the reprojection error equation as the optimization equation, and the poses of the remaining frames and map point poses with a high degree of common vision with the current frame are used as the optimization objects to construct a map.
- the optimizer optimizes the pose obtained by the tracking thread.
- the method for estimating the pose of an object with six degrees of freedom provided by the present invention has two biggest innovations:
- Dynamic probability propagation and dynamic point removal are completed in the tracking thread of visual SLAM.
- the semantic information provided by the Chinese fusion target detection network completes the update of the dynamic probability, removes the influence of dynamic objects in the environment on the SLAM system, and improves the accuracy of the SLAM system.
- the second is to comprehensively consider the semantic information provided by the target detection network in the removal of dynamic feature points, and consider the pose information of the feature points and 3D map points in the SLAM system, and use the semantic information as a priori condition for judgment.
- the pose information is used as a decisive condition to prevent the false rejection of static feature points and improve the robustness of the SLAM system.
- the results compared with the ORB-SLAM2 system are shown in Figure 7.
- the dotted line in Figure 7 is the actual value of the camera trajectory
- the solid line in the left picture is the predicted trajectory of ORB-SLAM2
- the solid line in the right picture is the predicted trajectory of the present invention. It can be seen that the right picture The predicted trajectory is closer to the true value of the trajectory.
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Abstract
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Claims (13)
- 一种适用于室内动态环境的视觉SLAM方法,其特征在于:包括:S1、获取包括环境的彩色图、深度图,将所获取的彩色图和深度图封装成帧,输入跟踪线程,进行特征提取和匹配,利用匹配关系计算动态概率传播结果,S2、根据动态概率,剔除当前帧中的动态特征点,保留静态特征点,优化当前帧的位姿,然后进行关键帧判定,S3、若当前帧满足关键帧条件,对该关键帧进行目标检测,S4、根据关键帧目标检测的结果,对图片做语义分割,确定属于动态物体的区域,更新与关键帧特征点对应的的3D地图点的动态概率,S5、对动态概率更新后的关键帧输入局部建图线程,对系统维护的共视图进行更新,提取局部共视图,对关键帧和地图点的位姿进行局部优化,S6、对当前帧进行回环检测与回环融合,消除系统的累积误差,利用回环融合结果更新本质图,并利用本质图对所有关键帧和地图点的位姿进行全局优化。
- 根据权利要求2所述的适用于室内动态环境的视觉SLAM方法,其特征在于:所述的3D地图点动态概率享有优选传播权。
- 根据权利要求1所述的适用于室内动态环境的视觉SLAM方法,其特征在于:在S1中:当前帧中任一特征点的动态概率首先受到与其成功匹配的关键帧特征点或3D地图点的动态概率的影响:对于匹配成功的特征点,关键帧特征点或3D地图点的动态概率通过一定权重更新当前帧中特征点的动态概率;当前帧中任一特征点在受到匹配成功的特征点的动态概率影响后,其动态概率还会受到邻域高内动态概率特征点的影响:高动态率特征点由动态概率极高或极低的特征点组成,高动态率特征点会影响以其为中心、以一定像素距离为半径的区域内其余特征点的动态概率。
- 根据权利要求1所述的适用于室内动态环境的视觉SLAM方法,其特征在于:所述的动态概率传播包括特征匹配方式传播、高可信度特征点区域影响方式传播。
- 根据权利要求1所述的适用于室内动态环境的视觉SLAM方法,其特征在于:在S2中:以最小化重投影误差为优化方程,当前帧位姿为优化对象构建位姿优化器,将动态概率高的特征点设置为外点,利用最小化重投影误差对当前帧位姿进行优化,将当前帧中重投影误差超过阈值的特征点作为外点去除。
- 根据权利要求6所述的适用于室内动态环境的视觉SLAM方法,其特征在于:非线性优化将进行多次,在每次优化后计算所有点的重投影误差,若重投影误差小于阈值则将该点设置为内点,反之将该点设置为外点,外点不参与下一次优化过程。
- 根据权利要求1所述的适用于室内动态环境的视觉SLAM方法,其特征在于:在S3中,利用神经网络对该关键帧进行目标检测,使用预训练好的神经网络进行目标检测,对目标检测网络判定为动态类别的物体,使用语义分割算法提取出该物体在图像中轮廓作为动态区域。
- 根据权利要求8所述的适用于室内动态环境的视觉SLAM方法,其特征在于:关键帧目标检测网络前向传播过程和视觉SLAM系统为两个不同的ROS系统下的工作节点,其间的交互通过ROS系统下话题和消息机制实现。
- 根据权利要求1所述的适用于室内动态环境的视觉SLAM方法,其特征在于:在S4中:提取出动态物体轮廓区域作为动态区域,动态区域内的特征点被认定为动态点,其余特征点被认定为静态特征点,更新被检测关键帧所有特征点和与之相匹配的3D地图点的动态概率。
- 根据权利要求1所述的适用于室内动态环境的视觉SLAM方法,其特征在于:在S4中:动态物体类别需根据系统使用环境进行设定,在室内环境下设置人,猫,狗三个物体类别为动态物体类别。
- 根据权利要求1所述的适用于室内动态环境的视觉SLAM方法,其特征在于:在S5中:通过图优化方法对关键帧和3D地图点的位姿进行局部优化,以最小化重投影误差方程为优化方程,与当前帧共视程度高的其余帧位姿和地图点位姿为优化对象,构建图优化器,优化跟踪线程所求得位姿。
- 一种适用于室内动态环境的视觉SLAM方法,其特征在于:S1、获取包括环境的彩色图、深度图,将所获取的彩色图和深度图封装成帧,输入跟踪线程,进行特征提取和匹配,利用匹配关系计算动态概率传播结果,动态概率传播由特征匹配方式传播和高可信度特征点区域影响方式传播组成,在特征匹配过程中:如当前帧的特征点x i与关键帧特征点成功匹配,则关键帧特征点的动态概率 传播到当前帧的特征点的动态概率P(x i)上,如当前帧的特征点x i与局部地图中的3D地图点X i匹配成功,则3D地图点的动 态概率P(X i)传播到当前帧的特征点的动态概率P(x i)上,并且3D地图点动态概率享有优选传播权,当前帧中任一特征点的动态概率首先受到与其成功匹配的关键帧特征点或3D地图点的动态概率的影响:对于匹配成功的特征点,关键帧特征点或3D地图点的动态概率通过一定权重更新当前帧中特征点的动态概率;当前帧中任一特征点在受到匹配成功的特征点的动态概率影响后,其动态概率还会受到邻域高内动态概率特征点的影响:高动态率特征点由动态概率极高或极低的特征点组成,高动态率特征点会影响以其为中心、以一定像素距离为半径的区域内其余特征点的动态概率,S2、根据动态概率,剔除当前帧中的动态特征点,保留静态特征点,优化当前帧的位姿,然后进行关键帧判定,以最小化重投影误差为优化方程,当前帧位姿为优化对象构建位姿优化器,将动态概率高的特征点设置为外点,利用最小化重投影误差对当前帧位姿进行优化,将当前帧中重投影误差超过阈值的特征点作为外点去除,非线性优化将进行多次,在每次优化后计算所有点的重投影误差,若重投影误差小于阈值则将该点设置为内点,反之将该点设置为外点,外点不参与下一次优化过程,S3、若当前帧满足关键帧条件,对该关键帧进行目标检测,利用神经网络对该关键帧进行目标检测,使用预训练好的神经网络进行目标检测,对目标检测网络判定为动态类别的物体,使用语义分割算法提取出该物体在图像中轮廓作为动态区域,S4、根据关键帧目标检测的结果,对图片做语义分割,确定属于动态物体的区域,更新与关键帧特征点对应的的3D地图点的动态概率,动态物体类别需根据系统使用环境进行设定,在室内环境下设置人,猫,狗三个物体类别为动态物体类别,提取出动态物体轮廓区域作为动态区域,动态区域内的特征点被认定为动态点,其余特征点被认定为静态特征点,更新被检测关键帧所有特征点和与之相匹配的3D地图点的动态概率,S5、对动态概率更新后的关键帧输入局部建图线程,对系统维护的共视图进行更新,提取局部共视图,对关键帧和地图点的位姿进行局部优化,通过图优化方法对关键帧和3D地图点的位姿进行局部优化,以最小化重投影误差方程为优化方程,与当前帧共视程度高的其余帧位姿和地图点位姿为优化对象,构建图优化器,优化跟踪线程所求得位姿,S6、对当前帧进行回环检测与回环融合,消除系统的累积误差,利用回环融合结果更新本质图,并利用本质图对所有关键帧和地图点的位姿进行全局优化。
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