Single camera-based object detection and tracking for mobile robots

Abstract

In order for a mobile robot to successfully navigate its environment, it must have knowledge about the objects in its immediate vicinity. The robot can use this information for localization, navigation and object avoidance. Among many sensors available for object detection we are primarily interested in camera-based vision for indoor robot navigation. In this work, we focus on using a single camera to detect objects in the field of view of the robot for the purpose of obstacle avoidance. In order to obtain an integrated robot obstacle avoidance and navigation technique, we investigate a modular approach. In the first module, we extend an appearance based object detection (ABOD) technique to automatically identify individual objects. We then extract strong corner features, overlaying them over the identified objects. This allows us to select a few representative corners for each object. In the second module, we attempt to group these strong corner features using a planar homography technique to define more natural features such as 'planes' for further processing. As an added feature, we utilize the strong corner features generated from module 1, the corresponding features in the next frame from module 2 and a basic optical flow technique for tracking these identified objects. In the third and final module, we obtain distance and heading information for each of obstacles as the robot avoids and navigates in an indoor environment. We show both simulation and actual results on a mobile robot for each of these three modules. We hope to integrate these three modules to obtain a single camera-based integrated robot obstacle avoidance and navigation technique in future.