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

Abstract

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%.

Real-time fusion study of raster maps based on feature matching

“…Disadvantages: (1) In the absence of initial pose estimation, robots collide sometimes, but the path will be planned again;…”

“…Simultaneous Localization and Mapping was proposed by SMITH et al in 1986 [1] , and the construction of the entire environment map is completed by a mobile robot equipped with one or several sensors to obtain environmental data and estimate the position and attitude of the robot. The development of single-robot SLAM has become more and more mature, and it is widely used in drones, unmanned vehicles, disaster relief and other fields.…”

Group-$k$ Consistent Measurement Set Maximization for Robust Outlier Detection

“…They select measurements by solving the maximum edge weight clique problem, a variant of the maximum clique problem used in PCM. Work done by Chen et al (2023) also solves a maximum edge weight clique problem, but combines multiple consistency metrics into a single consistency function. Chen et al (2022) use a enforce consistency on both a spatial and temporal basis.…”

“…Work done by Chen et al (2023) also solves a maximum edge weight clique problem, but combines multiple consistency metrics into a single consistency function. Chen et al (2022) use a enforce consistency on both a spatial and temporal basis. Enforcing consistency in a spatiotemporal manner allows them to significantly reduce the time to find a consistent set of measurements.…”

Loop Closure Prioritization for Efficient and Scalable Multi-Robot SLAM

“…Loop Closure Prioritization Systems: The system we present in this paper performs loop closure prioritization to support large-scale, multi-robot SLAM. At the time of publication we know of no other directly comparable system, however there is existing work in loop closure selection [8]- [10]. These systems perform an outlier rejection function by selecting the most consistent inliers from a set of loop closures.…”