TY - GEN
T1 - A fusion of dynamic occupancy grid mapping and multi-object tracking based on lidar and camera sensors
AU - Wang, Yuchun
AU - Wang, Boyang
AU - Wang, Xu
AU - Tan, Yingqi
AU - Qi, Jianyong
AU - Gong, Jianwei
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/27
Y1 - 2020/11/27
N2 - Establishing a grid map containing dynamic and static information is an essential work for further research on motion planning systems that consider the interactive effects of multiple traffic participants. The algorithms will become efficient if we could take advantage of the interrelationship between dynamic occupancy grid mapping and multi-object tracking. Therefore, the purpose of this paper is to achieve a synergistic improvement in the effects of mapping and tracking algorithms by constructing the association between object tracking and map updating based on the fusion of Lidar and image information. After the fusion of the original Lidar point cloud and category information based on image deep learning, the static and dynamic grid regions in the grid map are updated separately. Among them, the particle filtering algorithm applied for dynamic grid optimization update utilizes the initial information given by the object tracking algorithm, and the update results of the dynamic grid in turn give guidance information for object tracking. This paper not only demonstrates the optimization effect of particle filtering on dynamic grid update when object tracking fails but also discusses the effect of the dynamic occupancy grid map on multi-object tracking accuracy and efficiency. The results show that the proposed method can achieve the establishment of the dynamic occupancy grid map and multi-object tracking simultaneously.
AB - Establishing a grid map containing dynamic and static information is an essential work for further research on motion planning systems that consider the interactive effects of multiple traffic participants. The algorithms will become efficient if we could take advantage of the interrelationship between dynamic occupancy grid mapping and multi-object tracking. Therefore, the purpose of this paper is to achieve a synergistic improvement in the effects of mapping and tracking algorithms by constructing the association between object tracking and map updating based on the fusion of Lidar and image information. After the fusion of the original Lidar point cloud and category information based on image deep learning, the static and dynamic grid regions in the grid map are updated separately. Among them, the particle filtering algorithm applied for dynamic grid optimization update utilizes the initial information given by the object tracking algorithm, and the update results of the dynamic grid in turn give guidance information for object tracking. This paper not only demonstrates the optimization effect of particle filtering on dynamic grid update when object tracking fails but also discusses the effect of the dynamic occupancy grid map on multi-object tracking accuracy and efficiency. The results show that the proposed method can achieve the establishment of the dynamic occupancy grid map and multi-object tracking simultaneously.
KW - Dynamic occupancy grid mapping
KW - Fusion
KW - Multiobject tracking
UR - http://www.scopus.com/inward/record.url?scp=85098963047&partnerID=8YFLogxK
U2 - 10.1109/ICUS50048.2020.9274841
DO - 10.1109/ICUS50048.2020.9274841
M3 - Conference contribution
AN - SCOPUS:85098963047
T3 - Proceedings of 2020 3rd International Conference on Unmanned Systems, ICUS 2020
SP - 107
EP - 112
BT - Proceedings of 2020 3rd International Conference on Unmanned Systems, ICUS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Unmanned Systems, ICUS 2020
Y2 - 27 November 2020 through 28 November 2020
ER -