A direct method for locating the focus of expansion

A Robust Road Vanishing Point Detection Adapted to the Real-world Driving Scenes

Vanishing point (VP) provides extremely useful information related to roads in driving scenes for advanced driver assistance systems (ADAS) and autonomous vehicles. Existing VP detection methods for driving scenes still have not achieved sufficiently high accuracy and robustness to apply for real-world driving scenes. This paper proposes a robust motion-based road VP detection method to compensate for the deficiencies. For such purposes, three main processing steps often used in the existing road VP detection methods are carefully examined. Based on the analysis, stable motion detection, stationary point-based motion vector selection, and angle-based RANSAC (RANdom SAmple Consensus) voting are proposed. A ground-truth driving dataset including various objects and illuminations is used to verify the robustness and real-time capability of the proposed method. The experimental results show that the proposed method outperforms the existing motion-based and edge-based road VP detection methods for various illumination conditioned driving scenes.

Optical Flow Based Robot Obstacle Avoidance

In this paper we try to develop an algorithm for visual obstacle avoidance of autonomous mobile robot. The input of the algorithm is an image sequence grabbed by an embedded camera on the B21r robot in motion. Then, the optical flow information is extracted from the image sequence in order to be used in the navigation algorithm. The optical flow provides very important information about the robot environment, like: the obstacles disposition, the robot heading, the time to collision and the depth. The strategy consists in balancing the amount of left and right side flow to avoid obstacles, this technique allows robot navigation without any collision with obstacles. The robustness of the algorithm will be showed by some examples.

Correspondence-free determination of the affine fundamental matrix

Fundamental matrix estimation is a central problem in computer vision and forms the basis of tasks such as stereo imaging and structure from motion. Existing algorithms typically analyze the relative geometries of matched feature points identified in both projected views. Automated feature matching is itself a challenging problem. Results typically have a large number of false matches. Traditional fundamental matrix estimation methods are very sensitive to matching errors, which led naturally to the application of robust statistical estimation techniques to the problem. In this work, an entirely novel approach is proposed to the fundamental matrix estimation problem. Instead of analyzing the geometry of matched feature points, the problem is recast in the frequency domain through the use of Integral Projection, showing how this is a reasonable model for orthographic cameras. The problem now reduces to one of identifying matching lines in the frequency domain which, most importantly, requires no feature matching or correspondence information. Experimental results on both real and synthetic data are presented that demonstrate the algorithm is a practical technique for fundamental matrix estimation. The behavior of the proposed algorithm is additionally characterized with respect to input noise, feature counts, and other parameters of interest.

Recovering heading for visually guided navigadon in the presence of self-moving objects

I present a model for recovering the direction of heading of an observer who is moving relative to a scene that may contain self-moving objects. The model builds upon the work of Rieger & Lawton (1985) and Longuet-Higgins & Prazdny (1981), whose approach uses velocity differences computed in regions of high depth variation to locate the focus of expansion that indicates the observer’s heading direction. We present the results of computer simulations with natural and artificial images and relate the behaviour of the model to Psychophysical observations regarding heading judgements.

Recovering heading for visually-guided navigation

We present a model for recovering the direction of heading of an observer who is moving relative to a scene that may contain self-moving objects. The model builds upon an algorithm proposed by Rieger and Lawton, based on earlier work by Longuet-Higgins and Prazdny. The algorithm uses velocity differences computed in regions of high depth variation to locate the focus of expansion, which indicates the observer's heading direction. We relate the behavior of the model to psychophysical observations regarding the ability of human observers to judge heading direction, and show how the model copes with self-moving objects in the environment.