Road Approximation in Euclidean and v-Disparity Space: A Comparative Study

Sappa, Ángel D.; Herrero, Rosa; Dornaika, Fadi; Gerónimo, David; López, Antonio

doi:10.1007/978-3-540-75867-9_138

Cited by 9 publications

(4 citation statements)

References 6 publications

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“…This method accumulates 3-D points on the Y Z-plane and estimates the curve using an angle histogram of the near 3-D points and a curvature histogram of the far 3-D points. Sappa et al [4] compared various polynomial-based vertical road profile models (linear, quadratic, and cubic) in both v-disparity and Y Z-plane domains using synthetic scenes. Labayrade et al [5], Suhr et al [6], and Suhr and Jung [7] used a piecewise linear function model.…”

Section: Related Researchmentioning

confidence: 99%

Dense Stereo-Based Robust Vertical Road Profile Estimation Using Hough Transform and Dynamic Programming

Suhr

Jung

2015

IEEE Trans. Intell. Transport. Syst.

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This paper proposes a dense stereo-based robust vertical road profile estimation method. The vertical road profile is modeled by a cubic B-spline curve, which is known to be accurate and flexible but difficult to estimate under a large proportion of outliers. To robustly estimate a cubic B-spline curve, the proposed method utilizes a two-step strategy that initially estimates a piecewise linear function and then obtains a cubic B-spline curve based on the initial estimation result. A Hough transform and dynamic programming are utilized for estimating a piecewise linear function to achieve robustness against outliers and guarantee optimal parameters. In the experiment, a performance evaluation and comparison were conducted using three publicly available databases. The result shows that the proposed method outperforms three previous methods in all databases. In particular, its performance is superior to the others in the cases of a large proportion of outliers and road surfaces distant from the ego-vehicle.Index Terms-B-spline curve, dense stereo, dynamic programming, Hough transform, piecewise linear function, road profile, road surface, stereo vision.

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Section: Related Researchmentioning

confidence: 99%

Dense Stereo-Based Robust Vertical Road Profile Estimation Using Hough Transform and Dynamic Programming

Suhr

Jung

2015

IEEE Trans. Intell. Transport. Syst.

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“…Other works, as presented in [ 31 ], estimate directly the roadway profile in the plane

, simplifying the problem to an adjustment in two dimensions; then, the method is similar to the v-disparity, but replacing the disparity by the depth ( Z ). A comparison between both methods can be found in [ 32 ]. On the other hand, some works presented obtain information relative to the plane of the roadway ahead of the vehicle by calculating the homography between the images of the stereo pair [ 33 ].…”

Section: State Of the Artmentioning

confidence: 99%

Pose Self-Calibration of Stereo Vision Systems for Autonomous Vehicle Applications

Musleh

Martín

Armingol

et al. 2016

Sensors

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Nowadays, intelligent systems applied to vehicles have grown very rapidly; their goal is not only the improvement of safety, but also making autonomous driving possible. Many of these intelligent systems are based on making use of computer vision in order to know the environment and act accordingly. It is of great importance to be able to estimate the pose of the vision system because the measurement matching between the perception system (pixels) and the vehicle environment (meters) depends on the relative position between the perception system and the environment. A new method of camera pose estimation for stereo systems is presented in this paper, whose main contribution regarding the state of the art on the subject is the estimation of the pitch angle without being affected by the roll angle. The validation of the self-calibration method is accomplished by comparing it with relevant methods of camera pose estimation, where a synthetic sequence is used in order to measure the continuous error with a ground truth. This validation is enriched by the experimental results of the method in real traffic environments.

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“…U-V-disparity have been used for 3D road scene classification into surface planes characterizing the road's features. Sappa et al [17] introduced two road plane approximation techniques, the first utilizing U-disparity and Hough transform and the second using least squares fitting to find the optimal fitting plane for the whole point cloud data. The benefits of stereo vision compared to other sensing technologies have been emphasized in [14], which proposed another usage of U-V-disparity for the purpose of modelling an urban environment.…”

Section: Introductionmentioning

confidence: 99%

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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Road Approximation in Euclidean and v-Disparity Space: A Comparative Study

Cited by 9 publications

References 6 publications

Dense Stereo-Based Robust Vertical Road Profile Estimation Using Hough Transform and Dynamic Programming

Dense Stereo-Based Robust Vertical Road Profile Estimation Using Hough Transform and Dynamic Programming

Pose Self-Calibration of Stereo Vision Systems for Autonomous Vehicle Applications

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

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