2021/05/23 by Marco Visca, Visca, Marco, Sampo Kuutti +7 · 1 citation
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Robotic Locomotion and Control #Robotic Path Planning Algorithms #Robotics (cs.RO) #Robotics and Sensor-Based Localization #cs.CV #cs.LG #cs.RO
paper · pdf · doi:10.48550/arxiv.2105.10937
Accepted for inclusion in Towards Autonomous Robotic Systems Conference (TAROS) 2021
openalex publication_date 2021/05/23 · arxiv created 2021/07/24 · arxiv updated 2021/07/27 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Terrain traversability analysis plays a major role in ensuring safe robotic navigation in unstructured environments. However, real-time constraints frequently limit the accuracy of online tests especially in scenarios where realistic robot-terrain interactions are complex to model. In this context, we propose a deep learning framework trained in an end-to-end fashion from elevation maps and trajectories to estimate the occurrence of failure events. The network is first trained and tested in simulation over synthetic maps generated by the OpenSimplex algorithm. The prediction performance of the Deep Learning framework is illustrated by being able to retain over 94% recall of the original simulator at 30% of the computational time. Finally, the network is transferred and tested on real elevation maps collected by the SEEKER consortium during the Martian rover test trial in the Atacama desert in Chile. We show that transferring and fine-tuning of an application-independent pre-trained model retains better performance than training uniquely on scarcely available real data.