Robust ground plane induced homography estimation for wide angle fisheye cameras

Abstract

Knowledge of motion with respect to the ground plane is required in many computer vision applications such as obstacle avoidance, egomotion estimation, and online calibration. The homography matrix comprises motion as well as ground plane information. Estimation of the homography matrix is challenging, as measurements are often not only corrupted by sparse gross outliers, but might also contain other structures, which are inconsistent with the ground plane such as curbstones and sidewalks. Several well studied algorithms regarding the identification of sparse gross outliers already exist. However, identifying structural outliers remains a challenging problem due the outliers' inner coherence. In homography and plane estimation structural outliers often cause plane fits that do not correspond to any physical plane in the scene. We make use of the large field of view of fisheye cameras by exploiting that outlier identification can be performed more robustly in the near field where motion parallax vectors are large. More sensitive data can then be tested subsequently based on the preceding results. The main contribution of this paper is twofold. First, we present a statistical analysis of parallax amplitudes that are to be expected due to the distance of a point from the ground plane and measurement noise. This leads to a statistical test for outliers with local adaptive thresholds. Second, we embed this concept into an extended Kalman filter for efficient processing. Furthermore, we emphasize the importance of warping captured images into a common frame previous to feature detection and matching to avoid distortion effects and to equalize search regions. We demonstrate the robustness of our approach and the effects of prewarping on the estimation using real data.

Obstacle Detection Techniques in Outdoor Environment: Process, Study and Analysis

“…Surface normal were evaluated by considering gradients in disparity maps and were used to figure the attainable part. In 2014, Knorr et al [35] gave an approach in which large field of view of fish eye camera was used to ruggedly recognize outliers, where huge motion parallax vectors were present. They clubbed adaptive thresholding with expanded kalman filter for better execution.…”