DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams

Abstract

To achieve collaborative tasks, robots in a team need to have a shared understanding of the environment and their location within it. Distributed Simultaneous Localization and Mapping (SLAM) offers a practical solution to localize the robots without relying on an external positioning system (e.g. GPS) and with minimal information exchange. Unfortunately, current distributed SLAM systems are vulnerable to perception outliers and therefore tend to use very conservative parameters for inter-robot place recognition. However, being too conservative comes at the cost of rejecting many valid loop closure candidates, which results in less accurate trajectory estimates. This paper introduces DOOR-SLAM, a fully distributed SLAM system with an outlier rejection mechanism that can work with less conservative parameters. DOOR-SLAM is based on peerto-peer communication and does not require full connectivity among the robots. DOOR-SLAM includes two key modules: a pose graph optimizer combined with a distributed pairwise consistent measurement set maximization algorithm to reject spurious inter-robot loop closures; and a distributed SLAM front-end that detects inter-robot loop closures without exchanging raw sensor data. The system has been evaluated in simulations, benchmarking datasets, and field experiments, including tests in GPS-denied subterranean environments. DOOR-SLAM produces more inter-robot loop closures, successfully rejects outliers, and results in accurate trajectory estimates, while requiring low communication bandwidth. Full source code is available at https://github.com/MISTLab/DOOR-SLAM.git.

Graph‐based subterranean exploration path planning using aerial and legged robots

“…Accelerated by the kick‐off of the DARPA Subterranean Challenge, the domain of underground exploration path planning is currently experiencing rapid growth. Examples of this relate to the works in Miller et al (2020) using legged systems, a data set on relevant artifact classification (Shivakumar et al, 2019), a method on combining contact and inertial data for confined and degraded flying robot navigation (Lew et al, 2019), the contribution in Lajoie et al (2019) on robot localization, the lighter‐than‐air design in Huang et al (2019), as well as our team contributions in Bjelonic et al (2020), Dang, Mascarich, Khattak, Nguyen et al (2019), Dang, Mascarich, Khattak, Papachristos et al (2019), Khattak et al (2019), and Papachristos et al (2019). Motivated by this challenge, the contribution proposed in this paper aims to offer a general in nature, but also optimized in design, path planning methodology for autonomous subterranean exploration.…”

Swarm SLAM: Challenges and Perspectives

“…When mapping dynamic environments, if the valuable information is only the location at which a modification happened, a very schematic map could be sufficient and drastically reduce the amount of data to be shared. A few promising candidates to achieve fully decentralized swarm SLAM are distributed mapping ( Fox et al, 2006 ; Ghosh et al, 2020 ; Lajoie et al, 2020 ) and graph-based mapping ( Kümmerle et al, 2011 )—the latter seems particularly appropriate for building topological or semantic maps.…”

CHORD: Distributed Data-Sharing via Hybrid ROS 1 and 2 for Multi-Robot Exploration of Large-Scale Complex Environments

“…Multi-agent communication systems have been considered in various domains in robotics, including cooperative exploration [6], collaborative SLAM [9], [10], and ad-hoc communication infrastructure building [11]. However, it is still challenging to maintain a reliable system under harsh realworld constraints.…”