[Paper Review] Present and Future of SLAM in Extreme Underground Environments

Kamak Ebadi, Lukas Bernreiter+29 more|arXiv (Cornell University)|Aug 2, 2022

Robotics and Sensor-Based Localization38 citations

TL;DR

This paper surveys recent progress in underground SLAM, analyzes six SubT Challenge teams, and discusses open problems and future directions for LIDAR-centric, multi-robot SLAM in GPS-denied underground settings.

ABSTRACT

This paper reports on the state of the art in underground SLAM by discussing different SLAM strategies and results across six teams that participated in the three-year-long SubT competition. In particular, the paper has four main goals. First, we review the algorithms, architectures, and systems adopted by the teams; particular emphasis is put on lidar-centric SLAM solutions (the go-to approach for virtually all teams in the competition), heterogeneous multi-robot operation (including both aerial and ground robots), and real-world underground operation (from the presence of obscurants to the need to handle tight computational constraints). We do not shy away from discussing the dirty details behind the different SubT SLAM systems, which are often omitted from technical papers. Second, we discuss the maturity of the field by highlighting what is possible with the current SLAM systems and what we believe is within reach with some good systems engineering. Third, we outline what we believe are fundamental open problems, that are likely to require further research to break through. Finally, we provide a list of open-source SLAM implementations and datasets that have been produced during the SubT challenge and related efforts, and constitute a useful resource for researchers and practitioners.

Motivation & Objective

• Review the state of the art and practice of underground SLAM.
• Analyze single- and multi-robot SLAM architectures from SubT teams, with emphasis on LIDAR-centric approaches.
• Discuss maturity, feasible advances, and fundamental open problems in underground SLAM.
• Provide open-source SLAM implementations and datasets from SubT efforts as resources for researchers.

Proposed method

• Survey the literature on subterranean SLAM and SubT Challenge results.
• Describe and compare SLAM architectures from six SubT teams.
• Highlight design choices, sensor modalities, and system engineering aspects.
• Summarize open-source implementations and datasets produced during SubT and related efforts.

Experimental results

Research questions

• RQ1 What SLAM architectures and strategies were used by SubT teams for underground exploration?
• RQ2 How do LIDAR-centric SLAM and multi-robot collaboration perform in extreme subterranean environments?
• RQ3 What are the main challenges and open problems in underground SLAM that require further research?
• RQ4 What open-source resources (implementations and datasets) are available from SubT efforts for benchmarking?

Key findings

• LIDAR-centric SLAM approaches dominate the SubT systems across teams.
• Multi-robot SLAM architectures include centralized, decentralized, and distributed paradigms with shared maps and loop closures.
• Real-world underground operation is hindered by obscurants, limited lighting, and computational constraints requiring robust, modular fusion.
• Open challenges include sensor redundancy, parameter tuning, loop closure reliability in degenerate geometries, and robust multi-robot data exchange.
• Several teams provide open-source SLAM implementations and datasets to support benchmarking and research.
• The paper emphasizes practical, “dirty details” often omitted in papers, offering candid insights into system engineering trade-offs.