TY  - GEN
N2  - Simultaneous Localization and Mapping (SLAM) is one of the key issues for mobile robots
to achieve true autonomy. The implementations of SLAM could rely on a variety of sensors. Among many
types of them, the laser-based SLAM approach is widely used owing to its high accuracy, even in poor
lighting conditions. However, when in structure-less environments, laser modules will fail due to a lack
of sufficient geometric features. Besides, motion estimation by moving lidar has the problem of distortion
since range measurements are received continuously. To solve these problems, we propose a tightly-coupled
SLAM integrating LiDAR and an integrated navigation system (INS) for unmanned vehicle navigation in
campus environments. On the basis of feature extraction, a constraint equation for inter-frame point cloud
features is constructed, and the pose solution results of the INS are added as a priori data for inter-frame point
cloud registration. The Levenberg-Marquardt nonlinear least square method is used to solve the constraint
equation to obtain inter-frame pose relationships. Map matching and loop closure detection methods are used
to optimize the odometer, and the optimal pose information is obtained. The proposed SLAM algorithm
is evaluated by comparing with the classic open-source laser SLAM algorithms on the campus dataset.
Experimental results demonstrate that our proposed algorithm has certain advantages in estimating the
trajectory error of the unmanned vehicle and has higher mapping performance.
AB  - Simultaneous Localization and Mapping (SLAM) is one of the key issues for mobile robots
to achieve true autonomy. The implementations of SLAM could rely on a variety of sensors. Among many
types of them, the laser-based SLAM approach is widely used owing to its high accuracy, even in poor
lighting conditions. However, when in structure-less environments, laser modules will fail due to a lack
of sufficient geometric features. Besides, motion estimation by moving lidar has the problem of distortion
since range measurements are received continuously. To solve these problems, we propose a tightly-coupled
SLAM integrating LiDAR and an integrated navigation system (INS) for unmanned vehicle navigation in
campus environments. On the basis of feature extraction, a constraint equation for inter-frame point cloud
features is constructed, and the pose solution results of the INS are added as a priori data for inter-frame point
cloud registration. The Levenberg-Marquardt nonlinear least square method is used to solve the constraint
equation to obtain inter-frame pose relationships. Map matching and loop closure detection methods are used
to optimize the odometer, and the optimal pose information is obtained. The proposed SLAM algorithm
is evaluated by comparing with the classic open-source laser SLAM algorithms on the campus dataset.
Experimental results demonstrate that our proposed algorithm has certain advantages in estimating the
trajectory error of the unmanned vehicle and has higher mapping performance.
AD  - Chengdu University of Traditional Chinese Medicine
AD  - Chengdu University of Information Technology
AD  - Chengdu University of Information Technology
AD  - Chengdu University of Traditional Chinese Medicine
AD  - Chengdu University of Information Technology
AD  - Chengdu University of Information Technology
AD  - Chengdu University of Information Technology
AD  - University of Southern Indiana
T1  - Tightly-Coupled SLAM Integrating LiDAR and INS for Unmanned Vehicle Navigation in Campus Environments
DA  - 2024-02-19
AU  - Zhang, Linshuai
AU  - Wang, Qian
AU  - Gu, Shuoxin
AU  - Jiang, Tao
AU  - Jiang, Shiqi
AU  - Liu, Jiajia
AU  - Luo, Shuang
AU  - Yan, Gongjun
L1  - https://library.usi.edu/record/1492681/files/Tightly-Coupled_SLAM_Integrating_LiDAR_and_INS_for_Unmanned_Vehicle_Navigation_in_Campus_Environments%20%281%29.pdf
JF  - IEEE Access
LA  - eng
PY  - 2024-02-19
ID  - 1492681
L4  - https://library.usi.edu/record/1492681/files/Tightly-Coupled_SLAM_Integrating_LiDAR_and_INS_for_Unmanned_Vehicle_Navigation_in_Campus_Environments%20%281%29.pdf
KW  - SLAM
KW  - mobile robot
KW  - localization and navigation
KW  - multi-sensor data fusion
KW  - liDAR and INS
KW  - high-precision point cloud map
TI  - Tightly-Coupled SLAM Integrating LiDAR and INS for Unmanned Vehicle Navigation in Campus Environments
Y1  - 2024-02-19
L2  - https://library.usi.edu/record/1492681/files/Tightly-Coupled_SLAM_Integrating_LiDAR_and_INS_for_Unmanned_Vehicle_Navigation_in_Campus_Environments%20%281%29.pdf
LK  - https://library.usi.edu/record/1492681/files/Tightly-Coupled_SLAM_Integrating_LiDAR_and_INS_for_Unmanned_Vehicle_Navigation_in_Campus_Environments%20%281%29.pdf
UR  - https://library.usi.edu/record/1492681/files/Tightly-Coupled_SLAM_Integrating_LiDAR_and_INS_for_Unmanned_Vehicle_Navigation_in_Campus_Environments%20%281%29.pdf
ER  -