Laser-based Navigation Enhanced with 3D Time-of-Flight Data

Fang Yuan, Agnes Swadzba, Roland Philippsen, Orhan Engin, Marc Hanheide, and Sven Wachsmuth

Navigation and obstacle avoidance in robotics using planar laser scans has matured over the last decades. They basically enable robots to also penetrate highly dynamic and populated spaces, such as people’s home, and move around smoothly. However, in an unconstrained environment the twodimensional perceptual space of a fixed mounted laser is not sufficient in order to ensure safe navigation. In this paper, we present an approach that pools a fast and reliable motion generation approach with modern 3D capturing techniques using a Time-of-Flight camera. Instead of attempting to implement full 3D motion control, which is computationally more expensive and simply not needed for the targeted scenario of a domestic robot, we introduce a “virtual laser”. For the originally solely laser-based motion generation the technique of fusing real laser measurements and 3D point clouds into a continuous data stream is 100% compatible and transparent. The paper covers the general concept, the necessary extrinsic calibration of two very different types of sensors, and exemplarily illustrates the benefit which is to avoid obstacles not being perceivable in the original laser scan.


Pose Estimation and Map Building with a PMD-Camera for Robot Navigation

A. Prusak, O. Melnychuk, H. Roth, I. Schiller, R. Koch, 2007

pdficon_largeIn this paper we describe a joint approach for robot navigation with collision avoidance, pose estimation and map building with a 2.5D PMD (Photonic Mixer Device)-Camera combined with a high-resolution spherical camera. The cameras are mounted at the front of the robot with a certain inclination angle. The navigation and map building consists of two steps: When entering new terrain the robot first scans the surrounding. Simultaneously a 3D-panorama is generated from the PMD-images. In the second step the robot moves along the predefined path, using the PMD-camera for collision avoidance and a combined Structure-from-Motion (SfM) and model-tracking approach for self-localization. The computed poses of the robot are simultaneously used for map building with new measurements from the PMD-camera.