Building a qualitative local occupancy grid in a new vision-based reactive navigation strategy for mobile robots

Abstract

Reactive navigation strategies for mobile robots base their success on the ability to discriminate obstacles from the ground in the vicinity of the robot and many of these strategies are based uniquely on the computation of quantitative information. In this paper we describe a new method to build qualitative local occupancy grids that are used in a new vision-based navigation strategy addressed to mobile robots to explore safely unknown environments. The process includes a new feature classifier based on the Inverse Perspective Transformation to discriminate object from ground points and a method to determine angle and range with respect to the camera in the world coordinate system.

Robust ground plane region detection using multiple visual cues for obstacle avoidance of a mobile robot

“…As for ground region detection, pixel-level obstacle representations such as warping or inverse perspective transform (IPT) techniques have been widely studied in both stereo-based and monocularbased approaches. [12][13][14][15][16] This technique uses ground plane homography to warp one of the acquired images. Therefore, only pixels which correspond to the ground plane will match in both images.…”

Robust ground plane detection for obstacle avoidance of mobile robots using a monocular camera

“…For pixel level obstacles representation, warping or inverse perspective transform (IPT) technique has been widely used in both stereo-based and monocular-based approaches [10][11][12][13][14]. This technique uses ground plane homography to warp one of the images.…”