Lunar Rover Localization Using Craters as Landmarks
Not on the map. Why?
Abstract (arXiv)
Onboard localization capabilities for planetary rovers to date have used relative navigation, by integrating combinations of wheel odometry, visual odometry, and inertial measurements during each drive to track position relative to the start of each drive. At the end of each drive, a ground-in-the-loop (GITL) interaction is used to get a position update from human operators in a more global reference frame, by matching images or local maps from onboard the rover to orbital reconnaissance images or maps of a large region around the rover's current position. Autonomous rover drives are limited in distance so that accumulated relative navigation error does not risk the possibility of the rover driving into hazards known from orbital images. However, several rover mission concepts have recently been studied that require much longer drives between GITL cycles, particularly for the Moon. These concepts require greater autonomy to minimize GITL cycles to enable such large range; onboard global localization is a key element of such autonomy. Multiple techniques have been studied in the past for onboard rover global localization, but a satisfactory solution has not yet emerged. For the Moon, the ubiquitous craters offer a new possibility, which involves mapping craters from orbit, then recognizing crater landmarks with cameras and-or a lidar onboard the rover. This approach is applicable everywhere on the Moon, does not require high resolution stereo imaging from orbit as some other approaches do, and has potential to enable position knowledge with order of 5 to 10 m accuracy at all times. This paper describes our technical approach to crater-based lunar rover localization and presents initial results on crater detection using 3D point cloud data from onboard lidar or stereo cameras, as well as using shading cues in monocular onboard imagery.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 3 of its 24 references that are in the map. First posted on arXiv 18 Mar 2022.
Builds on
3 of 24 references.
References · 3
- 2014Microsoft COCO: Common Objects in Contextcited by 17,608
- 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
- 2018Object Detection with Deep Learning: A Reviewcited by 528
Built on by
Citing papers per year: 2023: 2; 2024: 1; 2025: 0; 2026: 1.
4 papers.
- 2023LunarNav: Crater-based Localization for Long-range Autonomous Lunar Rover Navigationcited by 6
- 2024ShadowNav: Autonomous Global Localization for Lunar Navigation in Darknesscited by 3
- 2023ShadowNav: Crater-Based Localization for Nighttime and Permanently Shadowed Region Lunar Navigationcited by 1
- 2026Globally Localizing Lunar Rover in Pixels via Graph Alignmentcited by 0
- 2026Globally Localizing Lunar Rover in Pixels via Graph Alignment9 Jun 2026
- 2024ShadowNav: Autonomous Global Localization for Lunar Navigation in Darkness2 May 2024
- 2023ShadowNav: Crater-Based Localization for Nighttime and Permanently Shadowed Region Lunar Navigation11 Jan 2023
- 2023LunarNav: Crater-based Localization for Long-range Autonomous Lunar Rover Navigation3 Jan 2023