Distributed Visual-Inertial Cooperative Localization

Abstract

In this paper we present a consistent and distributed state estimator for multi-robot cooperative localization (CL) which efficiently fuses environmental features and loopclosure constraints across time and robots. In particular, we leverage covariance intersection (CI) to allow each robot to only track its own state and autocovariance and compensate for the unknown correlations between robots. Two novel different methods for utilizing common environmental temporal SLAM features are introduced and evaluated in terms of accuracy and efficiency. Moreover, we adapt CI to enable drift-free estimation through the use of loop-closure measurement constraints to other robots' historical poses without a significant increase in computational cost. The proposed distributed CL estimator is validated against its naive non-realtime centralized counterpart extensively in both simulations and real-world experiments.

Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape

“…We observe that more exciting new directions are still being discovered, considering that recent approaches such as (Tian et al, 2021b) have been shown to outperform, both in accuracy and convergence rate, the well established Distributed Gauss-Seidel pose graph optimization method (Choudhary et al, 2017a) reused in many state-of-the-art C-SLAM systems such as (Cieslewski et al, 2018;Lajoie et al, 2020;Wang et al, 2019). Those promising approaches also include the majorization-minimization technique from (Fan and Murphey, 2020), the consensus-based 3D pose estimation technique inspired by distributed formation control from (Cristofalo et al, 2019;Cristofalo et al, 2020), and (Zhu et al, 2021) distributed estimator based on covariance intersection.…”