Visually-guided obstacle avoidance in unstructured environments

Lorigo, Liana M.; Brooks, Rodney A.; Grimsou, W.E.L.

doi:10.1109/iros.1997.649086

Cited by 102 publications

(84 citation statements)

References 10 publications

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“…39) [157], [158], [12], [81], [99]. Sometimes in these sorts of applications, the robot is supposed to just wander around, exploring the vicinity of the robot without a clearcut goal [91], [136]. However, in other applications, the task to be performed requires that the robot follow a specific path to a goal position.…”

Section: Unstructured Outdoor Navigationmentioning

confidence: 99%

“…Some of the earliest systems to use color to distinguish shadows from obstacles are by Thorpe et al [143] and Turk et al [151]. Another more recent example of this is in [91], where the authors have addressed the problem of vision-guided obstacle avoidance using three redundant vision modules, one for intensity (BW), one for RGB, and one for HSV. The goal is to figure out the position of obstacles in the scene while the robot wanders and explores the environment.…”

Section: Illumination and Outdoor Navigationmentioning

confidence: 99%

See 1 more Smart Citation

Vision for mobile robot navigation: a survey

DeSouza

Kak

2002

IEEE Trans. Pattern Anal. Machine Intell.

1,178

504

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Abstract-This paper surveys the developments of the last 20 years in the area of vision for mobile robot navigation. Two major components of the paper deal with indoor navigation and outdoor navigation. For each component, we have further subdivided our treatment of the subject on the basis of structured and unstructured environments. For indoor robots in structured environments, we have dealt separately with the cases of geometrical and topological models of space. For unstructured environments, we have discussed the cases of navigation using optical flows, using methods from the appearance-based paradigm, and by recognition of specific objects in the environment.

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Section: Unstructured Outdoor Navigationmentioning

confidence: 99%

Section: Illumination and Outdoor Navigationmentioning

confidence: 99%

Vision for mobile robot navigation: a survey

DeSouza

Kak

2002

IEEE Trans. Pattern Anal. Machine Intell.

1,178

504

View full text Add to dashboard Cite

show abstract

“…Similarly, the ALVINN neural network controlled the autonomous CMU Navlab using just 30 × 32 images as input [33]. Robust obstacle avoidance was achieved by Lorigo et al [23] using 64 × 48 images. In contrast to this historical work, our approach is driven not by hardware limitations by rather inspired by the limits of possibility, as in Torralba et al [40,41] and in Basu and Li [3], who argue that different resolutions should be used for different robotic tasks.…”

Section: Previous Workmentioning

confidence: 99%

Autonomous exploration using rapid perception of low-resolution image information

Murali

Birchfield

2011

Auton Robot

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We present a technique for mobile robot exploration in unknown indoor environments using only a single forward-facing camera. Rather than processing all the data, the method intermittently examines only small 32 × 24 downsampled grayscale images. We show that for the task of indoor exploration the visual information is highly redundant, allowing successful navigation even using only a small fraction of the available data. The method keeps the robot centered in the corridor by estimating two state parameters: the orientation within the corridor, and the distance to the end of the corridor. The orientation is determined by combining the results of five complementary measures, while the estimated distance to the end combines the results of three complementary measures. These measures, which are predominantly information-theoretic, are analyzed independently, and the combined system is tested in several unknown corridor buildings exhibiting a wide variety of appearances, showing the sufficiency of low-resolution visual information for mobile robot exploration. Because the algorithm discards such a large percentage of the pixels both spatially and temporally, processing occurs at an average of 1000 frames per second, thus freeing the processor for other concurrent tasks.

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“…When a robot is running in an unstructured environment such as a natural environment [8] or the surface of Mars [9], terrain classification and obstacle avoidance become the primary challenges [10]. In such cases, advanced sensors such as stereo cameras, Laser RADAR (LADAR), and appropriate sensor fusion techniques are necessary to deal with the complex environment [11]- [13].…”

Section: Related Workmentioning

confidence: 99%

“…Due to the inherent difficulties in understanding natural objects and changing environments, autonomous driving is still in its infancy. However, existing results such as motion planning with 3D vision and the use of multiple classifiers [10], [14] shed light on a different class of problems, where roads do not disappear completely.…”

Section: Related Workmentioning

confidence: 99%

Vision-based Motion Planning for an Autonomous Motorcycle on Ill-Structured Road

Song

Lee

et al. 2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-We report our development of a vision-based motion planning system for an autonomous motorcycle designed for desert terrain, where uniform road surface and lane markings are not present. The motion planning is based on a vision vector space (V 2 -Space), which is an unitary vector set that represents local collision-free directions in the image coordinate system. V 2 -Space is constructed by extracting the vectors based on the similarity of adjacent pixels, which captures both the color information and the directional information from prior vehicle tire tracks and pedestrian footsteps. We report how V 2 -Space is constructed to reduce the impact of varying lighting conditions in outdoor environments. We also show how V 2 -Space can be used to incorporate vehicle kinematic, dynamic, and timedelay constraints in motion planning to fit the highly dynamic requirements of the motorcycle. The combined algorithm of the V 2 -Space construction and the motion planning runs in O(n) time, where n is the number of pixels in the captured image. Experiments show that our algorithm outputs correct robot motion commands more than 90% of the time.

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Visually-guided obstacle avoidance in unstructured environments

Cited by 102 publications

References 10 publications

Vision for mobile robot navigation: a survey

Vision for mobile robot navigation: a survey

Autonomous exploration using rapid perception of low-resolution image information

Vision-based Motion Planning for an Autonomous Motorcycle on Ill-Structured Road

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