Principled ICP Covariance Modelling in Perceptually Degraded Environments for the EELS Mission Concept

• Published in: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 10763 - 10770
• Main Authors: Talbot, William, Nash, Jeremy, Paton, Michael, Ambrose, Eric, Metz, Brandon, Thakker, Rohan, Etheredge, Rachel, Ono, Masahiro, Ila, Viorela
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-10-2023
• Subjects: Optimized production technology; Pipelines; Propulsion; Saturn; Simultaneous localization and mapping; Uncertainty; Vents
• ISSN: 2153-0866
• DOI: 10.1109/IROS55552.2023.10341455

The Exobiology Extant Life Surveyor (EELS) is a snake-like mobile instruments platform under development at Jet Propulsion Laboratory (JPL) for a mission concept to find evidence of life on Saturn's sixth largest moon, Enceladus. To conduct a life surveying mission there, the EELS platform must first traverse an unknown icy surface terrain before undertaking a controlled descent into a cryovolcanic vent. The remoteness of Enceladus and the icy nature of its terrain demands a level of autonomy in navigation significantly higher than previous rover missions. The perception system onboard EELS must be highly resilient to perceptually-degraded environments such as flat, open ice fields, icy plumes, and repeating geometries in vents. EELS' perception system is implemented as a multi-sensor Simultaneous Localisation And Mapping (SLAM) solution called SERPENT. State Estimation through Robust Perception in Extreme and Novel Terrains (SERPENT) estimates the robot trajectory and maintains a map database, from which dense global or local maps can be obtained on demand for downstream planning algorithms. This system opts to incorporate measurements from many sensor modalities (laser scans, images, IMU, altimeter, etc.), solving the SLAM problem through joint optimisation, and thus requires that the contribution of each sensor be balanced through careful modelling of their uncertainties. With a specific focus on Light Detection And Ranging (LiDAR) in this context, this paper proposes a principled approach to model the covariances of point-to-plane Iterative Closest Point (ICP). It performs a rigorous comparative analysis of new and existing covariance models, and is the first time some of these have been tested within a complete SLAM pipeline. These models are evaluated on perceptually challenging datasets collected in glacial environments by the EELS sensor suite (see Figures 1, 2). SERPENT is open-sourced at https://github.com/jpl-eels/serpent.