TY - GEN
T1 - A Hybrid SLAM Method for Indoor Micro Aerial Vehicles
AU - Zheng, Yiwei
AU - Xu, Yang
AU - Zhang, Jinpeng
AU - Luo, Delin
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - In this paper, a new simultaneous localization and mapping (SLAM) method for micro aerial vehicles (MAVs) is put forward. Its main contributions are the hybrid iterative closest points and normal distribution transform (ICP-NDT) point cloud registration algorithm as well as the extended Kalman filter (EKF) algorithm for data fusion and estimation based on the dynamic model of the quadrotor. In this method, a 2-dimensional (2D) lidar is used to obtain surrounding obstacle information in the region. Its data can be turned into displacement by the hybrid ICP-NDT registration algorithm, and projected to a planar occupancy grid submap by the imaging algorithm. The displacement can be integrated into EKF for data fusion with the other sensors to get the optimal position for the MAV, and the submap can be inserted into this optimal position for updating the map. As the process repeats, the map can establish. The presented algorithm is tested in two pieces of the real scene, and the MAV is capable of getting its position and establishing the map for the region. In these tests, the maps can reflect the planar features of the environment with satisfactory accuracy.
AB - In this paper, a new simultaneous localization and mapping (SLAM) method for micro aerial vehicles (MAVs) is put forward. Its main contributions are the hybrid iterative closest points and normal distribution transform (ICP-NDT) point cloud registration algorithm as well as the extended Kalman filter (EKF) algorithm for data fusion and estimation based on the dynamic model of the quadrotor. In this method, a 2-dimensional (2D) lidar is used to obtain surrounding obstacle information in the region. Its data can be turned into displacement by the hybrid ICP-NDT registration algorithm, and projected to a planar occupancy grid submap by the imaging algorithm. The displacement can be integrated into EKF for data fusion with the other sensors to get the optimal position for the MAV, and the submap can be inserted into this optimal position for updating the map. As the process repeats, the map can establish. The presented algorithm is tested in two pieces of the real scene, and the MAV is capable of getting its position and establishing the map for the region. In these tests, the maps can reflect the planar features of the environment with satisfactory accuracy.
KW - 2D Lidar
KW - Extended Kalman Filter
KW - Mapping
KW - Micro Aerial Vehicle
KW - Scan Matching
UR - https://www.scopus.com/pages/publications/85075788515
U2 - 10.1109/ICCA.2019.8900012
DO - 10.1109/ICCA.2019.8900012
M3 - 会议稿件
AN - SCOPUS:85075788515
T3 - IEEE International Conference on Control and Automation, ICCA
SP - 651
EP - 656
BT - 2019 IEEE 15th International Conference on Control and Automation, ICCA 2019
PB - IEEE Computer Society
T2 - 15th IEEE International Conference on Control and Automation, ICCA 2019
Y2 - 16 July 2019 through 19 July 2019
ER -