The paper addresses the problem of multi-robot SLAM, where a team of collaborating robots aim to jointly estimate their trajectories and build a common map of the environment. The authors formulate this as a variational inference problem over a communication graph, with each robot maintaining a distribution over its own state (poses) and the shared environment (landmarks).
The key aspects of the approach are:
Formulation as a variational inference problem: The authors cast the multi-robot SLAM problem as minimizing the Kullback-Leibler (KL) divergence between a variational density and the true Bayesian posterior. This allows them to leverage tools from optimization and probabilistic inference.
Distributed mirror descent algorithm: The authors develop a distributed mirror descent algorithm to solve the variational inference problem. This involves each robot updating its density based on its own measurements, while also regularizing it to be similar to the densities of its neighbors in the communication graph.
Consensus on common landmarks: The authors introduce a consensus constraint on the estimates of common landmarks across robots. This is achieved by having the robots average the marginal densities of the shared landmarks during the mirror descent updates.
Distributed multi-state constraint Kalman filter (MSCKF): By using Gaussian distributions in the mirror descent algorithm, the authors derive a distributed version of the MSCKF algorithm, which combines visual odometry, feature observations, and object detections to jointly estimate robot poses and object maps.
The paper demonstrates the effectiveness of the proposed approach on both real-world (KITTI dataset) and simulated multi-robot scenarios. The results show that the distributed MSCKF with consensus averaging improves the overall accuracy of trajectories and object maps compared to individual SLAM, while also achieving better scaling to large robot teams compared to centralized approaches.
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by Hanwen Cao,S... às arxiv.org 04-30-2024
https://arxiv.org/pdf/2404.18331.pdfPerguntas Mais Profundas