U-V Disparity Analysis in Urban Environments

Musleh, Basam; Escalera, Arturo de la; Armingol, José María

doi:10.1007/978-3-642-27579-1_55

Cited by 20 publications

(25 citation statements)

References 8 publications

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“…There exist several segmentation methods to extract road surface using disparity image obtained by stereo camera [2][3][4]. The authors consider the U-disparity approach applied in [2] is a simple and effective approach for the purpose of the accurate road profile estimation, and regard as the base algorithm for improvement.…”

Section: Review Of the Conventional Approachmentioning

confidence: 99%

“…The authors consider the U-disparity approach applied in [2] is a simple and effective approach for the purpose of the accurate road profile estimation, and regard as the base algorithm for improvement.…”

Section: Review Of the Conventional Approachmentioning

confidence: 99%

“…In the cluttered environment where the road surface is not majority in the image, however, it is known that the result can be erroneous. For accurate and robust estimation, it is important to segment the road surface and input only the disparities of road surface to the road profile estimation [2,3].…”

Section: Introductionmentioning

confidence: 99%

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Road Surface Segmentation based on Vertically Local Disparity Histogram for Stereo Camera

et al. 2017

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In this paper, we propose a road surface segmentation technique which is accurate and suitable for hardware implementation. The road surface is segmented by detecting the boundary between road and obstacle, based on the disparity histogram which we define as VLDH (Vertically Local Disparity Histogram). On each pixel of disparity image, VLDH is computed from the disparities of vertically local neighbourhood pixels. The major advantage is the feasibility of the pipeline processing on image processing hardware for stereo camera. The direct advantage on processing time is confirmed based on implementation into FPGA. Experimental result also shows that the accuracy of the proposed method is better than the conventional method.

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Section: Review Of the Conventional Approachmentioning

confidence: 99%

Section: Review Of the Conventional Approachmentioning

confidence: 99%

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Road Surface Segmentation based on Vertically Local Disparity Histogram for Stereo Camera

et al. 2017

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“…The dense disparity map and the u-v disparity are developed in order to detect obstacles in front of the vehicle. The method for detecting obstacles and the free space in front of the vehicle has been presented in [11]. This method obtains, as a result, two different dense disparity maps.…”

Section: Obstacles and Road Detectionmentioning

confidence: 99%

“…An extended explanation of the solution to this problem is presented in [11]. For this reason, it is better to use the free map in order to generate the v-disparity because the obstacles are removed from the v-disparity, so it is easier to estimate the road profile.…”

Section: Estimation and Use Of The Road Profilementioning

confidence: 99%

Estimation and Prediction of the Vehicle’s Motion Based on Visual Odometry and Kalman Filter

Musleh

Martín

Escalera

et al. 2012

Advanced Concepts for Intelligent Vision Systems

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Abstract. The movement of the vehicle is an useful information for different applications, such as driver assistant systems or autonomous vehicles. This information can be known by different methods, for instance, by using a GPS or by means of the visual odometry. However, there are some situations where both methods do not work correctly. For example, there are areas in urban environments where the signal of the GPS is not available, as tunnels or streets with high buildings. On the other hand, the algorithms of computer vision are affected by outdoor environments, and the main source of difficulties is the variation in the ligthing conditions. A method to estimate and predict the movement of the vehicle based on visual odometry and Kalman filter is explained in this paper. The Kalman filter allows both filtering and prediction of vehicle motion, using the results from the visual odometry estimation.

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V-Disparity Based Obstacle Avoidance for Dynamic Path Planning of a Robot-Trailer

Tsiogas¹,

Kostavelis²,

Giakoumis³

et al. 2019

Lecture Notes in Computer Science

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Structured space exploration with mobile robots is imperative for autonomous operation in challenging outdoor applications. To this end, robots should be equipped with global path planners that ensure coverage and full exploration of the operational area as well as dynamic local planners that address local obstacle avoidance. The paper at hand proposes a local obstacle detection algorithm based on a fast stereo vision processing step, integrated with a dynamic path planner to avoid the detected obstacles in real-time, while simultaneously keeping track of the global path. It considers a robot-trailer articulated system, based on which the trailer trace should cover the entire operational space in order to perform a dedicated application. This is achieved by exploiting a model predictive controller to keep track of the trailer path while performing stereo vision-based local obstacle detection. A global path is initially posed that ensures full coverage of the operational space and during robot's motion, the detected obstacles are reported in the robot's occupancy grid map, which is considered from a hybrid global and local planner approach to avoid them locally. The developed algorithm has been evaluated in a simulation environment and proved adequate performance.

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U-V Disparity Analysis in Urban Environments

Cited by 20 publications

References 8 publications

Road Surface Segmentation based on Vertically Local Disparity Histogram for Stereo Camera

Road Surface Segmentation based on Vertically Local Disparity Histogram for Stereo Camera

Estimation and Prediction of the Vehicle’s Motion Based on Visual Odometry and Kalman Filter

V-Disparity Based Obstacle Avoidance for Dynamic Path Planning of a Robot-Trailer

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