Real-time obstacle avoidance using central flow divergence, and peripheral flow

Coombs, David; Herman, Martin; Hong, Tsai-Hong; Nashman, Marilyn

doi:10.1109/70.660840

Cited by 84 publications

(29 citation statements)

References 15 publications

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“…Generally, a path planning system needs a mapping of the surroundings to generate motion commands. However, a few systems do not explicitly model the nearby obstacles, but simply detect and avoid potentially hazardous conditions, for example using optical flow (Coombs, Herman, Hong, and Nashman, ; Santos‐Victor, Sandini, Curotto, and Garibaldi, ) or other visual features (i Badia, Pyk, and Verschure, ; Michels, Saxena, and Ng, ; Nakamura and Asada, ). A standard representation allows a system to utilize multiple perceptual modalities and naturally fuse them together.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Mobile Robot Localization and Navigation Using a Hierarchical Map Representation Primarily Guided by Vision

2014

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While impressive progress has recently been made with autonomous vehicles, both indoors and on streets, autonomous localization and navigation in less constrained and more dynamic environments, such as outdoor pedestrian and bicycle-friendly sites, remains a challenging problem. We describe a new approach that utilizes several visual perception modules-place recognition, landmark recognition, and road lane detectionsupplemented by proximity cues from a planar laser range finder for obstacle avoidance. At the core of our system is a new hybrid topological/grid-occupancy map that integrates the outputs from all perceptual modules, despite different latencies and time scales. Our approach allows for real-time performance through a combination of fast but shallow processing modules that update the map's state while slower but more discriminating modules are still computing. We validated our system using a ground vehicle that autonomously traversed three outdoor routes several times, each 400 m or longer, on a university campus. The routes featured different road types, environmental hazards, moving pedestrians, and service vehicles. In total, the robot logged over 10 km of successful recorded experiments, driving within a median of 1.37 m laterally of the center of the road, and localizing within 0.97 m (median) longitudinally of its true location along the route. C 2014 Wiley Periodicals, Inc.

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Section: Related Workmentioning

confidence: 99%

Autonomous Mobile Robot Localization and Navigation Using a Hierarchical Map Representation Primarily Guided by Vision

2014

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“…al., 2002;Dev, Kröse and Groen, 1997a;Baratoff, Toepfer and Neumann, 2000;Camus et. al., 1999;Coombs et. al., 1998;Santos-Victor et.…”

Section: Introductionmentioning

confidence: 99%

“…This paper addresses an application in mobile robot navigation that uses the optical flow technique to get all the information the robot needs to safely navigate. In some applications regarding the use of optical flow in mobile robot navigation, a special camera arrangement (Coombs et. al., 1998;Santos-Victor et.…”

Section: Introductionmentioning

confidence: 99%

“…al., 1995), a single special camera (Camus et. al., 1999;Coombs et. al., 1998) or specialized hardware pieces (Ancona and Pogio, 1995;Stöfler, Burkert and Färber, 2000;Carelli et.…”

Section: Introductionmentioning

confidence: 99%

“…al., 1998) or specialized hardware pieces (Ancona and Pogio, 1995;Stöfler, Burkert and Färber, 2000;Carelli et. al., 2002;Dev, Kröse and Groen, 1997a;Coombs et. al., 1998) has been adopted in order to get more accurate information or to accelerate the optical flow calculation.…”

Section: Introductionmentioning

confidence: 99%

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An optical flow-based sensing system for reactive mobile robot navigation

Caldeira¹,

Schneebeli²,

Sarcinelli-Filho³

2007

Sba Controle & Automação

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This work discusses the use of optical flow to generate the sensorial information a mobile robot needs to react to the presence of obstacles when navigating in a non-structured environment. A sensing system based on optical flow and time-to-collision calculation is here proposed and experimented, which accomplishes two important paradigms. The first one is that all computations are performed onboard the robot, in spite of the limited computational capability available. The second one is that the algorithms for optical flow and time-to-collision calculations are fast enough to give the mobile robot the capability of reacting to any environmental change in real-time. Results of real experiments in which the sensing system here proposed is used as the only source of sensorial data to guide a mobile robot to avoid obstacles while wandering around are presented, and the analysis of such results allows validating the proposed sensing system

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References

2016

Dictionary of Computer Vision and Image Processing

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Real-time obstacle avoidance using central flow divergence, and peripheral flow

Cited by 84 publications

References 15 publications

Autonomous Mobile Robot Localization and Navigation Using a Hierarchical Map Representation Primarily Guided by Vision

Autonomous Mobile Robot Localization and Navigation Using a Hierarchical Map Representation Primarily Guided by Vision

An optical flow-based sensing system for reactive mobile robot navigation

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