机器人行人跟随中的目标定位与无碰跟随研究
庞磊
2020-08-26
页数128
学位类型博士
中文摘要

行人跟随是移动机器人的一项重要能力,通过跟随指定行人运动既可以帮助行人运载重负载,也可以随时提供所需的服务,具有重要的理论研究意义和广泛应用前景。本文针对移动机器人行人跟随中的目标行人定位与无碰跟随开展研究,论文的主要内容如下:
首先,介绍了机器人行人跟随的研究背景和研究意义,从目标行人定位、机器人跟随方法、跟随过程中的干扰处理三方面进行了现状综述,并对论文内容和结构做了介绍。
其次,提出了基于视觉T-D-R的目标行人定位方法,以基于相关滤波器的视觉跟踪器为基础进行短时间的目标行人定位,并结合行人检测器和行人重识别模块解决其无法判断跟踪质量以及跟踪器失效后的跟踪恢复问题。行人重识别模块由CCF和IDLA模块组成,前者在线学习目标行人的卷积特征模板以及具有辨识能力的线性核相关滤波器,后者保存目标行人的近期瞬时图像并结合离线训练的端到端行人重识别网络确认行人身份,结合前者的稳定性和后者对瞬时大幅形变的适应性,满足长时鲁棒的跟随需求。所提方法能够较好地在多行人环境中识别出目标行人,有效地应对其他行人造成的遮挡干扰。与现有方法的对比实验以及四足机器人SCalf-III上的跟随实验表明了所提方法的有效性。
第三,提出了基于行人检测器和数据关联的目标行人定位方法,通过行人检测器提供目标行人的候选位置,并结合卡尔曼滤波器和行人重识别模块在相邻帧间关联行人检测结果以进一步确认目标行人身份。卡尔曼滤波器使用运动模型预测目标行人包围框,根据预测结果与行人检测结果之间的空间关系进行数据关联,进而综合行人重识别模块中基于孪生神经网络的SN模块和IDLA模块,衡量行人检测结果与目标行人的相似度以提高多行人环境中数据关联的可靠性。在此基础上,给出了一种激光点云分割算法以高效地获取非地面物体点云簇,进而在图像坐标系中将视觉行人语义信息与非地面物体激光点云簇融合起来,实现了3D行人定位和点云簇分类,通过实验进行了验证。
第四,提出了一种基于路径规划的无碰跟随方法。结合激光点云分割算法提供的非行人障碍信息生成静态障碍代价地图,并根据3D行人定位结果,利用基于UKF和最近邻联合概率数据关联的多行人跟踪,通过测量值和已有跟踪轨迹的配对实现干扰行人运动状态的连续估计,进而生成动态行人代价地图。在此基础上,全局规划器结合静态障碍代价地图输出指向目标行人的全局路径,而局部规划器也将动态行人代价地图纳入考虑范围,能够帮助机器人在躲避动态行人的同时尽量跟随全局路径。通过低频全局规划和高频局部规划结合的方式实现对目标行人安全、无碰的跟随,在室内外环境下进行了实验验证。
第五,针对目标行人长时间脱离机器人传感器视野而引起的跟随失败问题,提出了基于分布式里程计的目标行人定位框架。分别通过机器人端里程计和行人端里程计局部定位机器人和目标行人,动态融合两者提供的机器人位姿和目标行人位置以及目标行人3D定位信息获得两个里程计坐标系的转换关系。在此基础上,机器人根据行人端里程计的估计位置计算出目标行人在机器人端里程计坐标系下的位置,进而在目标行人脱离视野后,对估计位置进行采样并将其作为机器人的规划目标,通过无碰跟随方法以期重新找回目标行人。基于机器人端激光里程计和行人端穿戴式视觉-惯性里程计的具体实现进行实验,结果表明所提方法的远距离目标行人定位能力能够在目标行人脱离机器人传感器视野后实现目标找回和跟随恢复,具有较好的适应性。
最后,对本文工作进行了总结,并指出了需要进一步开展的研究工作。

英文摘要

Person following is an important ability for mobile robots, which can be applied for carrying heavy loads and offering immediate services by following a specified person. It is significant in both theoretical research and real-world applications. This thesis focuses on target locating and collision-free following for person-following robots. The contents are as follows:
Firstly, the research background and its significance of person following for mobile robots are given. The research development of target person locating, person-following approaches, and distraction-handling schemes are reviewed. The contents and structure of this thesis are also briefed.
Secondly, a visual T-D-R target person locating approach is proposed. The target person is located with a correlation filter-based visual tracker in a short time, and the problems of determining tracking quality and tracking recovery are solved by the combination of a pedestrian detector and a person re-identification module. The re-identification module is comprised of a CCF module and an IDLA module, where the former online learns convolutional features and a discriminative correlation filter with a linear kernel, and the latter stores several recent images of the target person and identifies the target person with an end-to-end person re-identification network. Combining the stable identification ability of the CCF module with the adaption to appearance variations of the IDLA module, the re-identification module can satisfy the requirement of long-term and robust tracking. The proposed approach can effectively distinguish target person in multi-pedestrian environments, which is verified by the comparisons with state-of-the-art approaches as well as the person-following experiment on the quadruped robot SCalf-III.
Thirdly, a target person locating approach based on a pedestrian detector and data association is presented, where the pedestrian detector is used to provide candidate locations of the target person, and a Kalman filter and a person re-identification module are employed to identify target person by associating detection results. The Kalman filter with a motion model is applied to predict the bounding box of the target person, which is then used to compute spatial relationships with detection results. The person re-identification module is introduced to improve the reliability of data association in multi-pedestrian environments, where the Siamese network-based SN module and IDLA module are integrated to measure the similarity between detection results and the target person. On this basis, an effective segmentation algorithm of LiDAR point cloud is given, which can be used to obtain non-ground object clusters. 3D pedestrian locating and clusters classification can be implemented by integrating visual pedestrian semantic information with non-ground clusters. The method is verified by the experiments.
Fourthly, a collision-free following approach based on path planning is proposed. The static obstacle cost map is generated according to the results of the point cloud segmentation algorithm. Multiple disturbing pedestrians are tracked based on unscented Kalman filter (UKF) and nearest near joint probabilistic data association (NNJPDA), and their motion states are estimated continuously by matching 3D pedestrian locations and tracking trajectories. Then, the dynamic pedestrian cost map is generated. On this basis, the global planner plans a global path with the static obstacle cost map, whereas the local planner also combines the dynamic pedestrian cost map for an optimized local path. This local path tends to follow the global path and avoid the collision with dynamic pedestrians. By combining low-frequency global planning with high-frequency local planning, the mobile robot achieves a safe and collision-free person following, which is verified in both indoor and outdoor environments.
Fifthly, aiming at the problem where the target person is beyond the field of view of robot sensors for a long time, a target person locating framework based on distributed odometries is proposed. The robot-end odometry and target-end odometry are used to estimate poses of the robot and positions of the target person, respectively, which is then integrated with 3D locations of the target person in robot coordinate to calculate the coordinate transformation between these two odometries. On this basis, the mobile robot can obtain the estimated target location by receiving the position information from the target-end odometry, which is then transformed to the coordinate in the robot-end odometry. When the mobile robot cannot locate the target person with its own sensors, it strives to re-detect the target person by moving along the sampling transformed positions using the collision-free following approach. With the robot-end LiDAR odometry and target-end wearable visual-inertial odometry, experiments are conducted and the results show that a mobile robot can find target person and recover person following even if the target person is far away from the robot.
Finally, conclusions and future work are offered.

关键词移动机器人 行人重识别 目标行人定位 运动状态估计 无碰跟随 分布式里程计
语种中文
七大方向——子方向分类智能机器人
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/40388
专题复杂系统管理与控制国家重点实验室_先进机器人
推荐引用方式
GB/T 7714
庞磊. 机器人行人跟随中的目标定位与无碰跟随研究[D]. 中国科学院自动化研究所. 中国科学院大学,2020.
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