# LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of Perceptually-Degraded Subterranean Environments

arXiv: https://arxiv.org/abs/2003.01744 · HTML: https://mlsanity.com/paper/2003.01744/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2020.

## Built on by (most cited first, 19 of 19)

- [NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge](https://mlsanity.com/paper/2103.11470/) (2021)
- [MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square](https://mlsanity.com/paper/2102.03771/) (2021)
- [STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation](https://mlsanity.com/paper/2103.02828/) (2021)
- [General Place Recognition Survey: Towards Real-World Autonomy](https://mlsanity.com/paper/2405.04812/) (2024)
- [DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization](https://mlsanity.com/paper/2212.06331/) (2022)
- [Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems](https://mlsanity.com/paper/2109.12343/) (2021)
- [General Place Recognition Survey: Towards the Real-world Autonomy Age](https://mlsanity.com/paper/2209.04497/) (2022)
- [AutoMerge: A Framework for Map Assembling and Smoothing in City-scale Environments](https://mlsanity.com/paper/2207.06965/) (2022)
- [DARE-SLAM: Degeneracy-Aware and Resilient Loop Closing in Perceptually-Degraded Environments](https://mlsanity.com/paper/2102.05117/) (2021)
- [UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search and Rescue in DARPA SubT](https://mlsanity.com/paper/2206.08185/) (2022)
- [Incremental Multimodal Surface Mapping via Self-Organizing Gaussian Mixture Models](https://mlsanity.com/paper/2309.10900/) (2023)
- [Autonomous Off-road Navigation over Extreme Terrains with Perceptually-challenging Conditions](https://mlsanity.com/paper/2101.11110/) (2021)
- [Early Recall, Late Precision: Multi-Robot Semantic Object Mapping under Operational Constraints in Perceptually-Degraded Environments](https://mlsanity.com/paper/2206.10062/) (2022)
- [GIRA: Gaussian Mixture Models for Inference and Robot Autonomy](https://mlsanity.com/paper/2307.00071/) (2023)
- [ROLO-SLAM: Rotation-Optimized LiDAR-Only SLAM in Uneven Terrain with Ground Vehicle](https://mlsanity.com/paper/2501.02166/) (2025)
- [CompSLAM: Complementary Hierarchical Multi-Modal Localization and Mapping for Robot Autonomy in Underground Environments](https://mlsanity.com/paper/2505.06483/) (2025)
- [Geometric Multi-Session Map Merging with Learned Local Descriptors](https://mlsanity.com/paper/2512.24384/) (2025)
- [MR-SLAM: Immersive Spatial Supervision for Multi-Robot Mapping via Mixed Reality](https://mlsanity.com/paper/2605.16432/) (2026)
- [Rethinking Learned Occupancy in Autonomous Active Mapping with Observation-Gated Filtering](https://mlsanity.com/paper/2609.09069/) (2026)

## Abstract (arXiv)

Simultaneous Localization and Mapping (SLAM) in large-scale, unknown, and complex subterranean environments is a challenging problem. Sensors must operate in off-nominal conditions; uneven and slippery terrains make wheel odometry inaccurate, while long corridors without salient features make exteroceptive sensing ambiguous and prone to drift; finally, spurious loop closures that are frequent in environments with repetitive appearance, such as tunnels and mines, could result in a significant distortion of the entire map. These challenges are in stark contrast with the need to build highly-accurate 3D maps to support a wide variety of applications, ranging from disaster response to the exploration of underground extraterrestrial worlds. This paper reports on the implementation and testing of a lidar-based multi-robot SLAM system developed in the context of the DARPA Subterranean Challenge. We present a system architecture to enhance subterranean operation, including an accurate lidar-based front-end, and a flexible and robust back-end that automatically rejects outlying loop closures. We present an extensive evaluation in large-scale, challenging subterranean environments, including the results obtained in the Tunnel Circuit of the DARPA Subterranean Challenge. Finally, we discuss potential improvements, limitations of the state of the art, and future research directions.