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Abstract-Navigation and obstacle avoidance in robotics using planar laser scans has matured over the last decades. They basically enable robots to 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 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 Timeof-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 laserbased 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.

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Mobile robots that are employed in people's homes need to safely navigate their environment. And natural human-inhabited environments still pose significant challenges for robots despite the impressive progress that has been achieved in the field of path planning and obstacle avoidance. These challenges mostly arise from the fact that (i) the perceptual abilities of a robot are limited, thus sometimes impeding its ability to see relevant obstacles (e.g. transparent objects), and (ii) the environment is highly dynamic being populated by humans. In this contribution we are making a case for an integrated solution to these challenges that builds upon the analysis and use of implicit human knowledge in path planning and a cascade of replanning approaches. We combine state of the art path planning and obstacle avoidance algorithms with the knowledge about how humans navigate in their very own environment. The approach results in a more robust and predictable navigation ability for domestic robots as is demonstrated in a number of experimental runs. ©2010 IEEE