An Objective Evaluation for Disparity Map Based on the Disparity Gradient and Disparity Acceleration

Zhang, Zhuoyun; Hou, Chunping; Shen, Lili; Yang, Jiachen

doi:10.1109/itcs.2009.98

Cited by 5 publications

(2 citation statements)

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“…The stereo vision system constructs a 3-D view of the environment and then extracts the obstacles present in it. This is done by either generating disparity maps and evaluating them [6] [7], or generating 3D point cloud map of the environment using the disparity values. Another way to accomplish the above is to use v-disparity method [8] -a well known real time obstacle and ground detection algorithm, which was used in DARPA Grand Challenge and proposed by R. Labayrade et al…”

Section: A 3d Point Cloudmentioning

confidence: 99%

Sensor Fusion of Laser & Stereo Vision Camera for Depth Estimation and Obstacle Avoidance

Kumar¹,

Gupta²,

Yadav³

2010

IJCA

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Laser Range Finders (LRF) have been widely used in the field of robotics to generate very accurate 2-D maps of environment perceived by Autonomous Mobile Robot. Stereo Vision devices on the other hand provide 3-D view of the surroundings with a range far much than of a LRF but at the tradeoff of accuracy. This paper demonstrates a technique of sensor fusion of information obtained from LRF and Stereovision camera systems to extract the accuracy and range of independents systems respectively. Pruning of the 3D point cloud obtained by the Stereo Vision Camera is done to achieve computational efficiency in real time environment, after which the point cloud model is scaled down to a 2-D vision map, to further reduce computational costs. The 2D map of the camera is fused with the 2D cost map of the LRF to generate a 2-D navigation map of the surroundings which in turn is passed as an occupancy grid to VFH+ for obstacle avoidance and path-planning. This technique has been successfully tested on "Lakshya"an IGV platform developed at Delhi College of Engineering in outdoor environments.

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Section: A 3d Point Cloudmentioning

confidence: 99%

Sensor Fusion of Laser & Stereo Vision Camera for Depth Estimation and Obstacle Avoidance

Kumar¹,

Gupta²,

Yadav³

2010

IJCA

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“…The disparity gradient and the disparity acceleration indices are presented in [20] to measure the smoothness of the DM. These indices require the use of thresholds.…”

Section: Introductionmentioning

confidence: 99%

A Measure for Accuracy Disparity Maps Evaluation

Cabezas

Padilla

Trujillo

2011

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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The quantitative evaluation of disparity maps is based on error measures. Among the existing measures, the percentage of Bad Matched Pixels (BMP) is widely adopted. Nevertheless, the BMP does not consider the magnitude of the errors and the inherent error of stereo systems, in regard to the inverse relation between depth and disparity. Consequently, different disparity maps, with quite similar percentages of BMP, may produce 3D reconstructions of largely different qualities. In this paper, a ground-truth based measure of errors in estimated disparity maps is presented. It offers advantages over the BMP, since it takes into account the magnitude of the errors and the inverse relation between depth and disparity. Experimental validations of the proposed measure are conducted by using two state-of-the-art quantitative evaluation methodologies. Obtained results show that the proposed measure is more suited than BMP to evaluate the depth accuracy of the estimated disparity map.

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