TY  - JOUR
A1  - Li, Ningbo
A1  - Guan, Lianwu
A1  - Gao, Yanbin
A1  - Du, Shitong
A1  - Wu, Menghao
A1  - Guang, Xingxing
A1  - Cong, Xiaodan
T1  - Indoor and outdoor low-cost seamless integrated navigation system based on the integration of INS/GNSS/LIDAR system
JF  - Remote Sensing
N2  - Global Navigation Satellite System (GNSS) provides accurate positioning data for vehicular navigation in open outdoor environment. In an indoor environment, Light Detection and Ranging (LIDAR) Simultaneous Localization and Mapping (SLAM) establishes a two-dimensional map and provides positioning data. However, LIDAR can only provide relative positioning data and it cannot directly provide the latitude and longitude of the current position. As a consequence, GNSS/Inertial Navigation System (INS) integrated navigation could be employed in outdoors, while the indoors part makes use of INS/LIDAR integrated navigation and the corresponding switching navigation will make the indoor and outdoor positioning consistent. In addition, when the vehicle enters the garage, the GNSS signal will be blurred for a while and then disappeared. Ambiguous GNSS satellite signals will lead to the continuous distortion or overall drift of the positioning trajectory in the indoor condition. Therefore, an INS/LIDAR seamless integrated navigation algorithm and a switching algorithm based on vehicle navigation system are designed. According to the experimental data, the positioning accuracy of the INS/LIDAR navigation algorithm in the simulated environmental experiment is 50% higher than that of the Dead Reckoning (DR) algorithm. Besides, the switching algorithm developed based on the INS/LIDAR integrated navigation algorithm can achieve 80% success rate in navigation mode switching.
KW  - vehicular navigation
KW  - GNSS/INS integrated navigation
KW  - INS/LIDAR integrated navigation
KW  - switching navigation
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-216229
SN  - 2072-4292
VL  - 12
IS  - 19
ER  - 
TY  - JOUR
A1  - Du, Shitong
A1  - Lauterbach, Helge A.
A1  - Li, Xuyou
A1  - Demisse, Girum G.
A1  - Borrmann, Dorit
A1  - Nüchter, Andreas
T1  - Curvefusion — A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration
JF  - Sensors
N2  - Mapping and localization of mobile robots in an unknown environment are essential for most high-level operations like autonomous navigation or exploration. This paper presents a novel approach for combining estimated trajectories, namely curvefusion. The robot used in the experiments is equipped with a horizontally mounted 2D profiler, a constantly spinning 3D laser scanner and a GPS module. The proposed algorithm first combines trajectories from different sensors to optimize poses of the planar three degrees of freedom (DoF) trajectory, which is then fed into continuous-time simultaneous localization and mapping (SLAM) to further improve the trajectory. While state-of-the-art multi-sensor fusion methods mainly focus on probabilistic methods, our approach instead adopts a deformation-based method to optimize poses. To this end, a similarity metric for curved shapes is introduced into the robotics community to fuse the estimated trajectories. Additionally, a shape-based point correspondence estimation method is applied to the multi-sensor time calibration. Experiments show that the proposed fusion method can achieve relatively better accuracy, even if the error of the trajectory before fusion is large, which demonstrates that our method can still maintain a certain degree of accuracy in an environment where typical pose estimation methods have poor performance. In addition, the proposed time-calibration method also achieves high accuracy in estimating point correspondences.
KW  - mapping
KW  - continuous-time SLAM
KW  - deformation-based method
KW  - time calibration
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-219988
SN  - 1424-8220
VL  - 20
IS  - 23
ER  -