A Novel Approach for Lidar-Based Robot Localization in a Scale-Drifted Map Constructed Using Monocular SLAM

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 10; p. 2230
• Main Authors: Wang, Su, Kobayashi, Yukinori, Ravankar, Ankit A, Ravankar, Abhijeet, Emaru, Takanori
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 14.05.2019; MDPI
• Subjects: Cameras; heterogeneous robot system; International conferences; Laser range finders; Lasers; Localization; Mapping; monocular SLAM; Robotics; Robots; scale drift; Sensors; Simultaneous localization and mapping; state estimation; Canada; United States > US; Japan; localization; state estimation; heterogeneous robot system; scale drift; monocular SLAM
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19102230

Scale ambiguity and drift are inherent drawbacks of a pure-visual monocular simultaneous localization and mapping (SLAM) system. This problem could be a crucial challenge for other robots with range sensors to perform localization in a map previously built by a monocular camera. In this paper, a metrically inconsistent priori map is made by the monocular SLAM that is subsequently used to perform localization on another robot only using a laser range finder (LRF). To tackle the problem of the metric inconsistency, this paper proposes a 2D-LRF-based localization algorithm which allows the robot to locate itself and resolve the scale of the local map simultaneously. To align the data from 2D LRF to the map, 2D structures are extracted from the 3D point cloud map obtained by the visual SLAM process. Next, a modified Monte Carlo localization (MCL) approach is proposed to estimate the robot's state which is composed of both the robot's pose and map's relative scale. Finally, the effectiveness of the proposed system is demonstrated in the experiments on a public benchmark dataset as well as in a real-world scenario. The experimental results indicate that the proposed method is able to globally localize the robot in real-time. Additionally, even in a badly drifted map, the successful localization can also be achieved.