A Survey on Terrain Traversability Analysis for Autonomous Ground Vehicles: Methods, Sensors, and Challenges
Paulo Borges, Thierry Peynot, Sisi Liang, Bilal Arain, Matthew Wildie, Melih Minareci, Serge Lichman, Garima Samvedi, Inkyu Sa, Nicolas Hudson, Michael Milford, Peyman Moghadam, Peter Corke
Abstract
Understanding the terrain in the upcoming path of a ground robot is one of the most challenging problems in field robotics. Terrain and traversability analysis is a multidisciplinary field combining robotics with image and signal processing, feature extraction, machine learning, three-dimensional (3D) mapping, and 3D geometry. Application scenarios range from autonomous vehicles on urban networks to agriculture, defence, exploration, mining, and search and rescue. Given the broad set of techniques available and the fast progress in this area, in this paper we organize and survey the corresponding literature, define unambiguous key terms, and discuss links among fundamental building blocks ranging from terrain classification to traversability regression. The advantages and the drawbacks of the methods are critically discussed, providing a comprehensive coverage of key aspects, including open code, available datasets for experimentation and comparisons, and important open research issues.