Fast Loop Closure Selection Method with Spatiotemporal Consistency for Multi-Robot Map Fusion

This paper presents a robust method based on graph topology to find the topologically correct and consistent subset of inter-robot relative pose measurements for multi-robot map fusion. However, the absence of good prior on relative pose gives a severe challenge to distinguish the inliers and outliers, and once the wrong inter-robot loop closures are used to optimize the pose graph, which can seriously corrupt the fused global map. Existing works mainly rely on the consistency of spatial dimension to select inter-robot measurements, while it does not always hold. In this paper, we propose a fast inter-robot loop closure selection method that integrates the consistency and topology relationship of inter-robot measurements, which both conform to the continuity characteristics of similar scenes and spatiotemporal consistency. Firstly, a clustering method integrating topology correctness of inter-robot loop closures is proposed to split the entire measurement set into multiple clusters. Then, our method decomposes the traditional high-dimensional consistency matrix into the sub-matrix blocks corresponding to the overlapping trajectory regions. Finally, we define the weight function to find the topologically correct and consistent subset with the maximum cardinality, then convert the weight function to the maximum clique problem from graph theory and solve it. We evaluate the performance of our method in a simulation and in a real-world experiment. Compared to state-of-the-art methods, the results show that our method can achieve competitive performance in accuracy while reducing computation time by 75%.

Combining Deep Learning and Robust Estimation for Outlier-Resilient Underwater Visual Graph SLAM

Visual Loop Detection (VLD) is a core component of any Visual Simultaneous Localization and Mapping (SLAM) system, and its goal is to determine if the robot has returned to a previously visited region by comparing images obtained at different time steps. This paper presents a new approach to visual Graph-SLAM for underwater robots that goes one step forward the current techniques. The proposal, which centers its attention on designing a robust VLD algorithm aimed at reducing the amount of false loops that enter into the pose graph optimizer, operates in three steps. In the first step, an easily trainable Neural Network performs a fast selection of image pairs that are likely to close loops. The second step carefully confirms or rejects these candidate loops by means of a robust image matcher. During the third step, all the loops accepted in the second step are subject to a geometric consistency verification process, being rejected those that do not fit with it. The accepted loops are then used to feed a Graph-SLAM algorithm. The advantages of this approach are twofold. First, the robustness in front of wrong loop detection. Second, the computational efficiency since each step operates only on the loops accepted in the previous one. This makes online usage of this VLD algorithm possible. Results of experiments with semi-synthetic data and real data obtained with an autonomous robot in several marine resorts of the Balearic Islands, support the validity and suitability of the approach to be applied in further field campaigns.

Using a Two-Stage Method to Reject False Loop Closures and Improve the Accuracy of Collaborative SLAM Systems

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%.