Lamp: Large-Scale Autonomous Mapping and Positioning for Exploration of Perceptually-Degraded Subterranean Environments
Abstract
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 multirobot 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. This research was the first step towards developing a U.S. Army Learning Organization Capability that will collectively consist of a tailored definition of learning organization specific to the Army context, an Army Learning Organization Maturity Model (ALOMM), an assessment (ALOA) for leaders to measure their organizations learning maturity, and resources to develop maturity as a learning organization based on assessment results. The objective of this first phase of research was to develop an ALOMM that describes what U.S. Army Learning Organizations (LOs) look like, and what they do.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2020
- Accession Number
- AD1146609
Entities
People
- Abhishek Thakur
- Alex Hatteland
- Alex Stephens
- Ali-akbar Agha-mohammadi
- Benjamin Morrell
- Eric Heiden
- Kamak Ebadi
- Luca Carlone
- Matteo Palieri
- Nobuhiro Funabiki
- Sally Wood
- Yun Chang
Organizations
- University of Southern California