Related papers: WayFAST: Traversability Predictive Navigation for Field Robots

WayFAST: Traversability Predictive Navigation for Field Robots

URL: http://arxiv.org/abs/2203.12071v1
Date: Tue, 22 Mar 2022 22:02:03 GMT
Title: WayFAST: Traversability Predictive Navigation for Field Robots
Authors: Mateus Valverde Gasparino, Arun Narenthiran Sivakumar, Yixiao Liu, Andres Eduardo Baquero Velasquez, Vitor Akihiro Hisano Higuti, John Rogers, Huy Tran, Girish Chowdhary
Abstract summary: We present a self-supervised approach for learning to predict traversable paths for wheeled mobile robots. Our key inspiration is that traction can be estimated for rolling robots using kinodynamic models. We show that our training pipeline based on online traction estimates is more data-efficient than other-based methods.
Score: 5.914664791853234
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We present a self-supervised approach for learning to predict traversable paths for wheeled mobile robots that require good traction to navigate. Our algorithm, termed WayFAST (Waypoint Free Autonomous Systems for Traversability), uses RGB and depth data, along with navigation experience, to autonomously generate traversable paths in outdoor unstructured environments. Our key inspiration is that traction can be estimated for rolling robots using kinodynamic models. Using traction estimates provided by an online receding horizon estimator, we are able to train a traversability prediction neural network in a self-supervised manner, without requiring heuristics utilized by previous methods. We demonstrate the effectiveness of WayFAST through extensive field testing in varying environments, ranging from sandy dry beaches to forest canopies and snow covered grass fields. Our results clearly demonstrate that WayFAST can learn to avoid geometric obstacles as well as untraversable terrain, such as snow, which would be difficult to avoid with sensors that provide only geometric data, such as LiDAR. Furthermore, we show that our training pipeline based on online traction estimates is more data-efficient than other heuristic-based methods.