Vision SLAM Maps: Towards Richer Content

Abstract

Simultaneous Localization and Mapping (SLAM) is a recursive probabilistic inferencing process for concurrently building a map of a robot’s surroundings and localizing that robot within this map. The ultimate goal of SLAM is to operate anywhere, allowing a robot to navigate autonomously and producing a meaningful purposeful map. Research in SLAM to date has focused on improving the localization part of SLAM, while lagging in the ability to produce useful maps. Indeed, all feature-based SLAM maps are built from either low level features such as points or lines or from artificial beacons; such maps have little use other than to perform SLAM. There are benefits in building maps from real natural objects that are indigenous of the environment for operations such as surveying of remote areas or as a guide for human navigation in dangerous settings. To investigate the potential of SLAM to produce such maps, an Inertial-Visual SLAM system is designed and used here which relies on inertial measurements to predict ego-motion and a digital camera to collect images of natural landmarks about the scene. Experiments conducted on a mobile vehicle show encouraging results and highlight the potential for Vision SLAM to generate meaningful maps which agree with ground truth. The Computer Vision system is capable of recognizing the environment type, of detecting trees within this environment, and recognizing different trees based on clusters of distinctive visual features.