Abstract
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.
Highlights
For a mobile robot operating in known or unknown areas, localization in a map of its work area is an essential requirement for several practical applications
This prior map used for localization is generated through a simultaneous localization and mapping (SLAM) process, in which the robot builds the map of the unknown environment based on the information from sensors while estimating its pose at the same time
The 2011-01-19-07-49-38, 2011-01-21-09-01-36, and 2011-04-06-38-27 in the MIT Stata dataset [39] are used. Since these sequences are acquired in the same building, it is possible to simulate all the necessary tasks, including the monocular SLAM process and localization tests, with these sequences
Summary
For a mobile robot operating in known or unknown areas, localization in a map of its work area is an essential requirement for several practical applications. In many cases, this prior map used for localization is generated through a simultaneous localization and mapping (SLAM) process, in which the robot builds the map of the unknown environment based on the information from sensors while estimating its pose at the same time. Monocular vision is not able to provide an absolute scale estimation of the landmarks. This leads to a problem that when performing monocular SLAM, the movement of the camera has to be fixed on an ambiguous scale factor.
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