Combining Laser-Scanning Data and Images for Target Tracking and Scene Modeling

Abstract

Working environments of modern robots have changed to unstructured, dynamic and outdoor scenes. There emerged several new challenges along with these changes, mainly in perception of both static and dynamic objects of the scenes. To tackle these new challenges, this research focused on study of advanced perception systems that can simultaneously model static scenes and track dynamic objects. Our research has three features. Multi-view and multi-type sensors, together with machine learning based algorithms, are utilized to obtain robust and reliable mapping/tracking results. In addition, a car-based mobile perception system is developed for exploring large sites. Finally, to improve robustness of the multi-view and mobile perception system, some new camera calibration methods are proposed. This paper presents an overview of our recent study on above mentioned ideas and technologies. Specifically we will focus on multi-sensor based multiple target tracking, simultaneous 3D mapping and target tracking in a mobile platform, and camera calibration.