Vulcan Centaur: towards end-to-end real-time perception in lunar rovers

TL;DR

This paper presents a real-time SLAM and VIO pipeline tailored for lunar rovers, utilizing prior lander information and image synthesis to enhance accuracy for planetary exploration.

Contribution

It introduces an object-level SLAM approach with pose and shape optimization, and employs image interpolation techniques for improved system precision in lunar rover navigation.

Findings

Successful implementation on Iris Lunar Rover simulation

Enhanced accuracy through image synthesis techniques

First lunar rover SLAM system integrating prior lander info

Abstract

We introduce a new real-time pipeline for Simultaneous Localization and Mapping (SLAM) and Visual Inertial Odometry (VIO) in the context of planetary rovers. We leverage prior information of the location of the lander to propose an object-level SLAM approach that optimizes pose and shape of the lander together with camera trajectories of the rover. As a further refinement step, we propose to use techniques of interpolation between adjacent temporal samples; videlicet synthesizing non-existing images to improve the overall accuracy of the system. The experiments are conducted in the context of the Iris Lunar Rover, a nano-rover that will be deployed in lunar terrain in 2021 as the flagship of Carnegie Mellon, being the first unmanned rover of America to be on the Moon.