Stereo based Obstacle Detection with Uncertainty in Rough Terrain

Mark, W. van der; Heuvel, J.C. van den; Groen, F.C.A.

doi:10.1109/ivs.2007.4290248

Cited by 23 publications

(17 citation statements)

References 9 publications

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“…Various studies have utilized clustering in the depth map for object detection, often using Euclidean distance to cluster point clouds into objects [128], [129]. Clustering was also used for object detection in [123].…”

Section: B Stereo Vision For Vehicle Detectionmentioning

confidence: 99%

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

802

322

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Abstract-This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding. Over the past decade, vision-based surround perception has progressed from its infancy into maturity. We provide a survey of recent works in the literature, placing vision-based vehicle detection in the context of sensor-based on-road surround analysis. We detail advances in vehicle detection, discussing monocular, stereo vision, and active sensor-vision fusion for on-road vehicle detection. We discuss vision-based vehicle tracking in the monocular and stereo-vision domains, analyzing filtering, estimation, and dynamical models. We discuss the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on-road behavior. We provide a discussion on the state of the art, detail common performance metrics and benchmarks, and provide perspective on future research directions in the field.

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Section: B Stereo Vision For Vehicle Detectionmentioning

confidence: 99%

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

802

322

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“…[8], where the image is divided into horizontal segments and sub-sampled according to the distance represented by such segments. In, 9 a set of discrete parameters was employed. Although these approaches are justifiable for noisy and low resolution range images (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…8 In practice, its only drawback is its high computational cost; it may take the method seconds to detect obstacles in a practical-sized range image. Researchers usually rely on approximations, [8][9][10] so it can perform within a reasonable time setting.…”

Section: Introductionmentioning

confidence: 99%

Real-time obstacle detection using range images: processing dynamically-sized sliding windows on a GPU

2015

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SUMMARYAn efficient obstacle detection technique is required so that navigating robots can avoid obstacles and potential hazards. This task is usually simplified by relying on structural patterns. However, obstacle detection constitutes a challenging problem in unstructured unknown environments, where such patterns may not exist. Talukder et al. (2002, IEEE Intelligent Vehicles Symposium, pp. 610-618.) successfully derived a method to deal with such environments. Nevertheless, the method has a high computational cost and researchers that employ it usually rely on approximations to achieve real-time. We hypothesize that by using a graphics processing unit (GPU), the computing time of the method can be significantly reduced. Throughout the implementation process, we developed a general framework for processing dynamically-sized sliding windows on a GPU. The framework can be applied to other problems that require similar computation. Experiments were performed with a stereo camera and an RGB-D sensor, where the GPU implementations were compared to multi-core and single-core CPU implementations. The results show a significant gain in the computational performance, i.e. in a particular instance, a GPU implementation is almost 90 times faster than a single-core one.KEYWORDS: Obstacle detection; Autonomous navigation; Stereo vision; Graphics processing unit (GPU). IntroductionA mobile robot with autonomous navigation capabilities can serve many practical purposes. It can be a vehicle that drives itself carrying passengers and goods or an agricultural machine that frees individuals from tedious and dangerous tasks. To accomplish such useful feat, robots must sense the environment through sensors, detect transversable regions and move through them precisely and efficiently. However, autonomous navigation involves several technical challenges, which range from dealing with intrinsically noisy sensors to correctly identifying road signs.A crucial component of an autonomous navigation system is the obstacle detection module. Regarding range images, a popular approach is to approximate the ground surface using a planar model. 1, 2 Once the model parameters have been estimated, the distance from any point to the model can be calculated and, based on a threshold, points can be distinguished as either parts of the ground or obstacles. Although reasonable for indoor environments, this approach is not suitable for outdoor ones, where one may expect to find curved and hilly regions.Another popular approach 3, 4 relies on a transformation of the disparity/range image called Vdisparity map, which is essentially a lateral projection of the range image where the longitudinal road profile is expected to appear as a line segment. With the road longitudinal profile extracted,

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“…Various studies have utilized clustering in the depth map for object detection, often using euclidean distance to cluster point clouds into objects [66], [67]. Clustering was also used for object detection in [68].…”

Section: A Appearance-based Approachesmentioning

confidence: 99%

A review of recent developments in vision-based vehicle detection

Sivaraman¹,

Trivedi²

2013

2013 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-This document provides a review of the past decade's literature in on-road vision-based vehicle detection. Over the past decade, vision-based surround perception has matured significantly from its infancy. We detail advances in vehicle detection, discussing representative works from the monocular and stereo-vision domains. We provide discussion on the state-of-the-art, and provide perspective on future research directions in the field.

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Stereo based Obstacle Detection with Uncertainty in Rough Terrain

Cited by 23 publications

References 9 publications

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Real-time obstacle detection using range images: processing dynamically-sized sliding windows on a GPU

A review of recent developments in vision-based vehicle detection

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