Obstacle Detection and Terrain Classification for Autonomous Off-Road Navigation

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A multirange architecture for collision‐free off‐road robot navigation (2009, background): “Thanks to the layered architecture, not only is the planning executed once per level, but also the mapping and recentering, which is a costly process in the case of the hyperbolic polar map.”
RELLIS-3D Dataset: Data, Benchmarks and Analysis (2020, background): “The importance of semantic awareness has led to the emergence of visual perception systems that directly feed information to navigation systems to complement depth sensors [1], [4], [5], [6], [7].”
Toward Reliable Off Road Autonomous Vehicles Operating in Challenging Environments (2006, background): “More recent work has concentrated on the prospects for learning terrain parameters on-line (Iagnemma et al. 2004; Ojeda et al. 2006; Manduchi et al. 2005).”
SIFT and color feature fusion using localized maximum-margin learning for scene classification (2011, background): “Moreover, recognizing the scene category of a place is a rather important ability for an intelligent vehicle or robot to take correct actions under the scene [4, 5] (e.”
Terrain traversability analysis methods for unmanned ground vehicles: A survey (2013, background): “In Manduchi et al. (2005), a hybrid classification scheme was proposed that acted upon the output of a positive obstacle detection process.”