TY - GEN
T1 - Multi-Sensors Based Simultaneous Mapping and Global Pose Optimization
AU - Wang, Chaoran
AU - Zhai, Yong
AU - Xu, Shaohang
AU - Gong, Jianwei
AU - Xiong, Guangming
AU - Qi, Jianyong
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - In recent years, 3D Lidar Based Simultaneous Localization and Mapping (SLAM) has become a hotspot in the research of the autonomous vehicle navigation. In this paper, we proposed a multi-sensors based 3D SLAM system to get more accurate and robust SLAM results in real time. Firstly, the information from different sensors is added into the data-association to reduce the cumulative error of pose. Then, we use the IMU (Inertial Measurement Unit) information complementary filtering to improve the accuracy and efficiency of scan matching and loop closure detection. What's more, a two-layer loop closure detection algorithm is proposed in our research to improve the real-time performance of the loop closure detection. We have carried a lot of experiments and the experimental results shows that our method is more accurate and better in real-time performance compared with the state-of-the-art algorithm.
AB - In recent years, 3D Lidar Based Simultaneous Localization and Mapping (SLAM) has become a hotspot in the research of the autonomous vehicle navigation. In this paper, we proposed a multi-sensors based 3D SLAM system to get more accurate and robust SLAM results in real time. Firstly, the information from different sensors is added into the data-association to reduce the cumulative error of pose. Then, we use the IMU (Inertial Measurement Unit) information complementary filtering to improve the accuracy and efficiency of scan matching and loop closure detection. What's more, a two-layer loop closure detection algorithm is proposed in our research to improve the real-time performance of the loop closure detection. We have carried a lot of experiments and the experimental results shows that our method is more accurate and better in real-time performance compared with the state-of-the-art algorithm.
KW - SLAM
KW - complementary filtering
KW - multi-sensors
KW - two-layer loop closure detection
UR - http://www.scopus.com/inward/record.url?scp=85080967226&partnerID=8YFLogxK
U2 - 10.1109/ICUS48101.2019.8995947
DO - 10.1109/ICUS48101.2019.8995947
M3 - Conference contribution
AN - SCOPUS:85080967226
T3 - Proceedings of the 2019 IEEE International Conference on Unmanned Systems, ICUS 2019
SP - 599
EP - 604
BT - Proceedings of the 2019 IEEE International Conference on Unmanned Systems, ICUS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Unmanned Systems, ICUS 2019
Y2 - 17 October 2019 through 19 October 2019
ER -