Study on Path Planning Method Considering Localization Accuracy for Exploration Rover

学位の種別: 修士University of Tokyo(東京大学

Rover Relocalization for Mars Sample Return by Virtual Template Synthesis and Matching

We consider the problem of rover relocalization in the context of the notional Mars Sample Return campaign. In this campaign, a rover (R1) needs to be capable of autonomously navigating and localizing itself within an area of approximately 50 x 50 m using reference images collected years earlier by another rover (R0). We propose a visual localizer that exhibits robustness to the relatively barren terrain that we expect to find in relevant areas, and to large lighting and viewpoint differences between R0 and R1. The localizer synthesizes partial renderings of a mesh built from reference R0 images and matches those to R1 images. We evaluate our method on a dataset totaling 2160 images covering the range of expected environmental conditions (terrain, lighting, approach angle). Experimental results show the effectiveness of our approach. This work informs the Mars Sample Return campaign on the choice of a site where Perseverance (R0) will place a set of sample tubes for future retrieval by another rover (R1).Comment: To appear in IEEE Robotics and Automation Letters (RA-L) and IEEE International Conference on Robotics and Automation (ICRA 2021

Long-range Rover Localization by Matching Lidar Scans to Orbital Elevation Maps

Current planetary rover localization techniques are lacking in autonomy and accuracy. An autonomous method of globally localizing a rover is proposed by matching features extractedvfrom a 3D orbital elevation map and rover-based 3D lidar scans. Localization can be further improved by including odometry measurements as well as orientation measurements from an inclinometer and sun sensor. The methodology was tested with real data from a Mars-Moon analogue site on Devon Island, Nunavut. By tying 23 real scans together with simulated odometry over a 10km traverse, the algorithm was able to localize with varying degrees of accuracy. Output uncertainties were large due to large input uncertainties, but these could be reduced in future experimentation by minimizing the use of simulated input data. It was concluded that the architecture could be used to accurately and autonomously localize a rover over long-range traverses.MAS