Stereo vision and rover navigation software for planetary exploration

By Steven. B. Goldberg, Mark W. Maimone, Larry H. Matthies. Proceedings, IEEE Aerospace Conference, 2002

470 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Astronomy and Astrophysics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/2a1b69be3d2dae9ebd388e81927f2b086431dc7b

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Citations in the corpus

Off-Road Obstacle Avoidance through End-to-End Learning (2005, methodology): “Such methods have been used successfully for many years for navigation in indo or environments where edge features can be reliably detected and matched [1], but navig ation in outdoors environment, despite a long history, is still a challenge [14, 3]: real-ti me stereo algorithms are considerably le”
Deep belief net learning in a long-range vision system for autonomous off-road driving (2008, methodology): “Many methods for vision-based navigation rely on stereobased obstacle detection [9], [11], [3].”
Visual odometry (2004, background): “Even since this result is not so surprising, other systems such as nasa:s rover navigation software[6] do not use it.”
Natural terrain classification using three‐dimensional ladar data for ground robot mobility (2006, background): “The classes are: ”scatter” to represent porous volumes such as grass and tree canopy, ”linear” to capture thinobjects like wires or tree branches, and finally ”surface” to capture solid objects like ground surface, rocks or largetrunks.”
High Speed Navigation of Unrehearsed Terrain: Red Team Technology for Grand Challenge 2004 (2004, methodology): “The terrain evaluation part of this module uses a statistical evaluation technique akin to that used in the Morphin/Gestalt algorithms [8][18].”
Real-time stereo visual odometry for autonomous ground vehicles (2008, background): “Nister [12], for example, describes a monocular approach that emphasizes feature tracking over small image motions, and demonstrates the effectiveness of this algorithm for realtime motion estimation on a variety of ground vehicles.”
Visual Navigation for Mobile Robots: A Survey (2008, background): “[45] introduced a stereo vision-based navigation algorithm for the rover planetary explorer MER, to explore and map locally hazardous terrains.”
Learning long‐range vision for autonomous off‐road driving (2009, methodology): “Many methods for vision-based navigation rely on stereo-based obstacle detection (Kelly and Stentz, 1998; Kriegman et al., 1989; Goldberg et al., 2002).”
A multirange architecture for collision‐free off‐road robot navigation (2009, methodology): “At Layer 1, knowing the current translation and rotation relative to the Layer-2 map, the globally optimal path can be computed using the locally optimal paths reaching all points at a fixed radius (5 m).”
Behavior-based robot navigation on challenging terrain: A fuzzy logic approach (2002, background): “Simmons et al. [6], Goldberg et al. [ 7 ], and Singh et al. [8] compute an analytical traversability measure for the terrain, based on stereo range data.”
Terrain traversability analysis methods for unmanned ground vehicles: A survey (2013, methodology): “By building upon fine terrain descriptions extracted from the GESTALT system (Goldberg et al., 2002), step, roughness, pitch and border hazards were perceived as described in Helmick et al. (2009), by using the statistics within a goodness map that quantified traversability by locally fitting…”