Path Planning for Motion Dependent State Estimation on Micro Aerial Vehicles

Markus W. Achtelik, Stephan Weiss, Margarita Chli, Roland Siegwart

Research output: Chapter in Book/Report/Conference proceedingConference contribution


With navigation algorithms reaching a certain maturity in the field of mobile robots, the community now focuses on more advanced tasks like path planning towards increased autonomy. While the goal is to efficiently compute a path to a target destination, the uncertainty in the robot's perception cannot be ignored if a realistic path is to be computed. With most state of the art navigation systems providing the uncertainty in motion estimation, here we propose to exploit this information. This leads to a system that can plan safe avoidance of obstacles, and more importantly, it can actively aid navigation by choosing a path that minimizes the uncertainty in the monitored states. Our proposed approach is applicable to systems requiring certain excitations in order to render all their states observable, such as a MAV with visual-inertial based localization. In this work, we propose an approach which takes into account this necessary motion during path planning: by employing Rapidly exploring Random Belief Trees (RRBT), the proposed approach chooses a path to a goal which allows for best estimation of the robot's states, while inherently avoiding motion in unobservable modes. We discuss our findings within the scenario of vision-based aerial navigation as one of the most challenging navigation problem, requiring sufficient excitation to reach full observability.
Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Robotics and Automation (ICRA)
Pages3926 - 3932
Publication statusPublished - 2013


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