RAVON: The robust autonomous vehicle for off-road navigation

Armbrust, Christopher; Braun, Tim; Föhst, Tobias; Proetzsch, Martin; Renner, Alexander; Schäfer, Bernd-Helge; Berns, Karsten

doi:10.1533/9780857090201.3.353

Cited by 20 publications

(15 citation statements)

References 38 publications

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“…stereo camera, error is proportional to square of distance). Further development of sensor technologies 20 which provide a long range dense point cloud, or methods of extrapolating short range point clouds to longer ranges, will be necessary for high-speed travel since the UGV must at least be able to have time to come to a complete stop after dangerous terrain features are detected (e.g. stopping distance for a typical car is 55m at 96km/h [29]).…”

Section: Discussionmentioning

confidence: 99%

“…In the most common case geometric approaches identify obstacles using a point cloud of the terrain elevations for detection of both positive and negative features [20]- [22]. Another geometric approach by Lu et al used a 2D laser line stripper to extract spatial frequency and texture information about the terrain to perform four class classification of the terrain with over 90% accuracy [23].…”

Section: Related Workmentioning

confidence: 99%

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Terrain Roughness Identification for High-Speed UGVs

Wilson¹,

Ramírez-Serrano²

2014

JACR

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Terrain Roughness Identification for High-Speed UGVs

Wilson¹,

Ramírez-Serrano²

2014

JACR

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“…Obviously the scanning performance of a full-featured 360°3D point cloud sensor like the Velodyne HDL-64E S2 [17] meets the requirements of mobile robotic system in a favorable way but unfortunately this specific sensor type is very costly. Therefore various research groups [2,11,18] including our research group chose to use actuated planar laser scanners to be able to realize a complete 3D scene sampling by rotating a planar laser scanner around a single axis. Figure 2 shows the picture of the design of the rotatable planar laser scanner implemented on our Autonomous Mobile Outdoor Robot (AMOR) [8,9] (see Fig.…”

Section: Point Cloud Acquisitionmentioning

confidence: 99%

Sensor-Fusion Based Real-Time 3D Outdoor Scene Reconstruction and Analysis on a Moving Mobile Outdoor Robot

Kuhnert¹,

Kuhnert²

2011

Künstl Intell

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This article presents an overview over the whole process of generating a precise and rich 3D representation of the local environment of a moving mobile outdoor robot. The resulting model of this process is a camera image textured triangle mesh which is triangulated from a motioncorrected laser scanned 3D point cloud. The demanding requirements of autonomous off-road environment model acquisition are handled by applying a multi-sensor fusion approach. Additionally to the model creation process a novel way of detecting and describing feature points on a piecewise linear 3D surfaces is presented. The set of feature points generated by the proposed method is a valuable abstraction of a whole outdoor scene that can be used in several following processing steps like mesh simplification or segmentation and robotics-specific tasks like obstacle detection or classification. All described methods are implemented and successfully used on the award-winning robot AMOR.

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“…Among LAGRderived work, [9] and [10] stand out for explicitly looking for path-like corridors of homogeneous color or texture along the ground. The European ELROB competitions have also required path-following skills; one robot effectively followed paths by finding "passages" among scattered trees in ladar data [11]. An approach to non-parametric trail detection using color + intensity saliency maps and agents was recently presented in [12] and extended to tracking in [13].…”

Section: Introductionmentioning

confidence: 99%

Integrating stereo structure for omnidirectional trail following

Rasmussen

Kocamaz

2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-We describe a system which follows "trails" for autonomous outdoor robot navigation. Through a combination of appearance and structural cues derived from stereo omnidirectional color cameras, the algorithm is able to detect and track rough paths despite widely varying tread material, border vegetation, and illumination conditions. The approaching trail region is modeled as a circular arc segment of constant width. Using likelihood formulations which measure color, brightness, and/or height contrast between a hypothetical region and flanking areas, the tracker performs a robust randomized search for the most likely trail region and robot pose relative to it with no a priori appearance model. The addition of the structural information, which is derived from a semi-global dense stereo algorithm with ground-plane fitting, is shown to improve trail segmentation accuracy and provide an additional layer of safety beyond solely ladar-based obstacle avoidance. Our system's ability to follow a variety of trails is demonstrated through live runs as well as analysis of offline runs on several long sequences with diverse appearance and structural characteristics using ground-truth segmentations.

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RAVON: The robust autonomous vehicle for off-road navigation

Cited by 20 publications

References 38 publications

Terrain Roughness Identification for High-Speed UGVs

Terrain Roughness Identification for High-Speed UGVs

Sensor-Fusion Based Real-Time 3D Outdoor Scene Reconstruction and Analysis on a Moving Mobile Outdoor Robot

Integrating stereo structure for omnidirectional trail following

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