A Robust 2D-SLAM Technology With Environmental Variation Adaptability

Simultaneous localization and mapping (SLAM) in complicated indoor/outdoor unknown environments is challenging. With a demand on high mobility and high integrity intelligent robotics, it is desired that the SLAM system should be portable and possibly standalone. To carry out the pose estimation as w...

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Bibliographic Details
Published in:IEEE sensors journal 2019-12, Vol.19 (23), p.11475-11491
Main Authors: Chen, Li-Hsin, Peng, Chao-Chung
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Coders
Ground truth
Image segmentation
Indoor environments
Inertial platforms
iterative closest point (ICP)
Laser radar
Lidar
light detection and ranging (LiDAR)
loop closure
occupancy grid map
Pose estimation
Principal components analysis
Robotics
Shaft encoders
Simultaneous localization and mapping
Simultaneous localization and mapping (SLAM)
Two dimensional displays
Unknown environments
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Summary:Simultaneous localization and mapping (SLAM) in complicated indoor/outdoor unknown environments is challenging. With a demand on high mobility and high integrity intelligent robotics, it is desired that the SLAM system should be portable and possibly standalone. To carry out the pose estimation as well as the mapping without relying on the information from other sensors, such as image, inertial measurement unit, rotary encoder of ground vehicle and so on, a single 2D light detection and ranging (LiDAR) is considered in this paper. In order to fulfill a robust 2D SLAM technology in unknown environments, the principal component analysis (PCA) is utilized to evaluate LiDAR scan contours and to carry out a corridor detector. The corridor detector is further extended to achieve adaptive unstable points removal, mapping probability adjustment as well as loop closure. Based on an adaptive grid map segmentation scheme, the cumulative mapping errors can obviously be reduced and a precise 2D map can be eventually carried out. Many experiments are conducted to verify the proposed method. Finally, for comparison, this paper utilizes the scan data and ground truth provided by the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT), to verify the localization precision of the proposed algorithm. Experiment shows that from the scan data in the route up to about 350 m, the maximum error can be as low as about 20 cm.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2019.2931368