Resilient and Distributed Multi-Robot Visual SLAM: Datasets, Experiments, and Lessons Learned (2304.04362v1)

Abstract: This paper revisits Kimera-Multi, a distributed multi-robot Simultaneous Localization and Mapping (SLAM) system, towards the goal of deployment in the real world. In particular, this paper has three main contributions. First, we describe improvements to Kimera-Multi to make it resilient to large-scale real-world deployments, with particular emphasis on handling intermittent and unreliable communication. Second, we collect and release challenging multi-robot benchmarking datasets obtained during live experiments conducted on the MIT campus, with accurate reference trajectories and maps for evaluation. The datasets include up to 8 robots traversing long distances (up to 8 km) and feature many challenging elements such as severe visual ambiguities (e.g., in underground tunnels and hallways), mixed indoor and outdoor trajectories with different lighting conditions, and dynamic entities (e.g., pedestrians and cars). Lastly, we evaluate the resilience of Kimera-Multi under different communication scenarios, and provide a quantitative comparison with a centralized baseline system. Based on the results from both live experiments and subsequent analysis, we discuss the strengths and weaknesses of Kimera-Multi, and suggest future directions for both algorithm and system design. We release the source code of Kimera-Multi and all datasets to facilitate further research towards the reliable real-world deployment of multi-robot SLAM systems.

• The paper enhances Kimera-Multi with novel distributed loop closure detection and robust pose graph optimization, enabling reliable multi-robot SLAM in challenging environments.
• The paper releases comprehensive benchmarking datasets from live MIT campus experiments, offering valuable reference trajectories and maps for performance evaluation.
• The paper validates system resilience under varying communication scenarios, demonstrating improved scalability and operational reliability compared to centralized approaches.

Insights into Resilient and Distributed Multi-Robot Visual SLAM

This essay presents an overview of the paper "Resilient and Distributed Multi-Robot Visual SLAM: Datasets, Experiments, and Lessons Learned," which examines the advancements and challenges associated with Kimera-Multi, a cutting-edge distributed multi-robot Simultaneous Localization and Mapping (SLAM) system. Authored by a team including Yulun Tian, Yun Chang, Long Quang, Arthur Schang, Carlos Nieto-Granda, Jonathan P. How, and Luca Carlone, the paper focuses on the practical deployment issues of multi-robot SLAM solutions in real-world settings, emphasizing robustness against communication dropouts and operational reliability in diverse environments.

Key Contributions

The paper delineates three primary contributions:

1. Enhancements in Kimera-Multi: The authors describe modifications to Kimera-Multi, improving its resilience in real-world scenarios with unreliable communication. These improvements are crucial for scalable deployment, allowing robots to maintain functionality even in distributed or dynamically changing communication networks.
2. Release of Benchmarking Datasets: The team has gathered comprehensive multi-robot datasets during live experiments on the MIT campus, offering scenarios that feature complex visual ambiguities and dynamic entities. These datasets are valuable for benchmarking, providing accurate reference trajectories and maps for validation purposes.
3. Evaluation Under Varying Communication Scenarios: The system's resilience is validated through experiments that simulate different communication conditions. These experiments demonstrate Kimera-Multi's capabilities compared to a centralized baseline system, detailing the setup, parameters, and metrics involved in assessing SLAM performance.

Technical Insights

System Improvements: The authors articulate enhancements focusing on distributed loop closure detection and robust pose graph optimization. The system implements a novel front-end capable of rapidly identifying loop closures through parallelization, thus circumventing the central bottleneck observed in centralized systems. Moreover, a distributed back-end employs a robust optimization framework suitable for processing imperfect data with GNC-based techniques.

Communication Infrastructure: The paper introduces a remote topic manager to facilitate communication among robots, allowing dynamic networking essential for distributed SLAM. This module effectively manages the constraints imposed by sporadic connectivity, a common problem in real-world scenarios.

Datasets and Experiments: The released datasets are instrumental in capturing operational challenges presented in urban environments, featuring both indoor and outdoor scenes. The experiments conducted with these datasets offer a comprehensive evaluation, highlighting practical issues with scaling and the impact of communication disruptions on the overall SLAM pipeline.

Results and Implications

The empirical results of the research reveal the distributed system's competency over centralized approaches, especially under the constraints of real-world communications. While the centralized approach quickly integrates information in stable communication networks, the distributed method retains robustness through isolated operation and local optimizations, proving its utility in dynamic environments with intermittent connectivity.

Predictive Future Trajectories in AI and Robotics: The advancements illustrated in this paper suggest promising improvements in deploying autonomous multi-robot systems across various sectors, such as logistics, military operations, and urban planning. As communication technology enhances, we can anticipate more refined integration of distributed SLAM systems in multi-robot fleets, leveraging real-time collaboration while maintaining individual autonomy.

Conclusion

The research conducted on Kimera-Multi paves the way for significant advancements in resilient multi-robot systems, underlining the necessity of robustness in the face of real-world challenges. By introducing an open-source architecture and benchmarking datasets, this work encourages further exploration and augmentation of distributed SLAM systems for more seamless incorporation into diverse, communication-limited environments. These ongoing developments inch closer to achieving reliable autonomy in multi-robot structures, facilitating broader AI deployment initiatives in complex real-life conditions.