Abstract
Loop-closure detection is an essential means to reduce accumulated errors of simultaneous localization and mapping (SLAM) systems. However, even false positive loop closures could seriously interfere and even corrupt the back-end optimization process. For a collaborative SLAM system that generally uses both intra-robot and inter-robot loop closures to optimize the pose graph, it is a tough job to reject those false positive loop closures without a reliable a priori knowledge of the relative pose transformation between robots. Aiming at this solving problem, this paper proposes a two-stage false positive loop-closure rejection method based on three types of consistency checks. Firstly, a multi-robot pose-graph optimization model is given which transforms the multi-robot pose optimization problem into a maximum likelihood estimation model. Then, the principle of the false positive loop-closure rejection method based on χ2 test is proposed, in which clustering is used to reject those intra-robot false loop-closures in the first step, and a largest mutually consistent loop-based χ2 test is constructed to reject inter-robot false loop closures in the second step. Finally, an open dataset and synthetic data are used to evaluate the performance of the algorithms. The experimental results demonstrate that our method improves the accuracy and robustness of the back-end pose-graph optimization with a strong ability to reject false positive loop closures, and it is not sensitive to the initial pose at the same time. In the Computer Science and Artificial Intelligence Lab (CSAIL) dataset, the absolute position error is reduced by 55.37% compared to the dynamic scaling covariance method, and the absolute rotation error is reduced by 77.27%; in the city10,000 synthetic dataset, the absolute position error is reduced by 89.37% compared to the pairwise consistency maximization (PCM) and the absolute rotation error is reduced by 97.9%.
Highlights
This paper proposes a two-stage false loop-closure rejection method based on three types of consistency checks, which can be embedded into the collaborative simultaneous localization and mapping (SLAM) system to improve the accuracy of the whole system
From the results of position and pose error of each algorithm on the City10,000 data set in Table 2 and Figure 8a–f, one can see that: (1) the dynamic covariance scaling (DCS) algorithm is less effective, the position error still reaches 5.6 m even when the parameters are adjusted to the optimal value; (2) the pairwise consistency maximization (PCM) algorithm still has a large gap with the true value under the condition of 90% confidence level; (3) with the confidence level being the same as that of PCM, the accuracy of position and pose estimation of our algorithm are much better than that of DCS and PCM
System that generally uses both intra and inter loop closures to optimize pose graph, it is difficult to reject those false positive loop closures without reliable a priori knowledge of the relative pose transformation among robots. Aiming at solving this problem, this paper proposes a two-stage false positive loop-closure rejection method based on three types of consistency checks that can be embedded in the collaborative SLAM system
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. In a collaborative SLAM system, generally, the pairwise internal consistency information a collaborative SLAM system, generally, the pairwise internal consistency information bebetween the loop measurement value and the local odometer measurement could be used tween the loop measurement value and the local odometer measurement could be used to reject false inter-robot loop closure; since there is usually a lack of mutual pose to reject false inter-robot loop closure; since there is usually a lack of mutual transformation a priori knowledge, it is a big challenge to concurrently run both intra-robot and inter-robot loop-closure rejection Aiming at this problem, this paper proposes a two-stage false loop-closure rejection method based on three types of consistency checks, which can be embedded into the collaborative SLAM system to improve the accuracy of the whole system. The proposed approach is easy to port to existing popular collaborative SLAM systems
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