2021/05/05 by Riccardo Giubilato, Mallikarjuna Vayugundla, Giubilato, Riccardo +9
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #FOS: Computer and information sciences #Robotic Path Planning Algorithms #Robotics (cs.RO) #Robotics and Sensor-Based Localization
paper · pdf · doi:10.48550/arxiv.2105.02020
openalex publication_date 2021/05/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Future planetary missions will rely on rovers that can autonomously explore and navigate in unstructured environments. An essential element is the ability to recognize places that were already visited or mapped. In this work, we leverage the ability of stereo cameras to provide both visual and depth information, guiding the search and validation of loop closures from a multi-modal perspective. We propose to augment submaps that are created by aggregating stereo point clouds, with visual keyframes. Point clouds matches are found by comparing CSHOT descriptors and validated by clustering, while visual matches are established by comparing keyframes using Bag-of-Words (BoW) and ORB descriptors. The relative transformations resulting from both keyframe and point cloud matches are then fused to provide pose constraints between submaps in our graph-based SLAM framework. Using the LRU rover, we performed several tests in both an indoor laboratory environment as well as a challenging planetary analog environment on Mount Etna, Italy. These environments consist of areas where either keyframes or point clouds alone failed to provide adequate matches demonstrating the benefit of the proposed multi-modal approach.