Header

UZH-Logo

Maintenance Infos

Perception-aware Receding Horizon Navigation for MAVs


Zhang, Zichao; Scaramuzza, Davide (2018). Perception-aware Receding Horizon Navigation for MAVs. In: IEEE International Conference on Robotics and Automation (ICRA), 2018., Brisbane, 1 May 2018 - 25 May 2018, 1-8.

Abstract

To reach a given destination safely and accurately, a micro aerial vehicle needs to be able to avoid obstacles and minimize its state estimation uncertainty at the same time. To achieve this goal, we propose a perception-aware receding horizon approach. In our method, a single forwardlooking camera is used for state estimation and mapping. Using the information from the monocular state estimation and mapping system, we generate a library of candidate trajectories and evaluate them in terms of perception quality, collision probability, and distance to the goal. The best trajectory to execute is then selected as the one that maximizes a reward function based on these three metrics. To the best of our knowledge, this is the first work that integrates active vision within a receding horizon navigation framework for a goal reaching task. We demonstrate by simulation and real-world experiments on an actual quadrotor that our active approach leads to improved state estimation accuracy in a goal-reaching task when compared to a purely-reactive navigation system, especially in difficult scenes (e.g., weak texture). A video showing the experiments can be found at https://youtu.be/761zxZMeQNo A narrated video presentation can be found here: https://www.youtube.com/watch?v=FK6S_CRXiuI

Abstract

To reach a given destination safely and accurately, a micro aerial vehicle needs to be able to avoid obstacles and minimize its state estimation uncertainty at the same time. To achieve this goal, we propose a perception-aware receding horizon approach. In our method, a single forwardlooking camera is used for state estimation and mapping. Using the information from the monocular state estimation and mapping system, we generate a library of candidate trajectories and evaluate them in terms of perception quality, collision probability, and distance to the goal. The best trajectory to execute is then selected as the one that maximizes a reward function based on these three metrics. To the best of our knowledge, this is the first work that integrates active vision within a receding horizon navigation framework for a goal reaching task. We demonstrate by simulation and real-world experiments on an actual quadrotor that our active approach leads to improved state estimation accuracy in a goal-reaching task when compared to a purely-reactive navigation system, especially in difficult scenes (e.g., weak texture). A video showing the experiments can be found at https://youtu.be/761zxZMeQNo A narrated video presentation can be found here: https://www.youtube.com/watch?v=FK6S_CRXiuI

Statistics

Downloads

3 downloads since deposited on 22 Mar 2018
3 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:25 May 2018
Deposited On:22 Mar 2018 12:18
Last Modified:14 Apr 2018 16:18
Publisher:IEEE
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Official URL:http://rpg.ifi.uzh.ch/docs/ICRA18_Zhang.pdf
Other Identification Number:merlin-id:16270

Download

Download PDF  'Perception-aware Receding Horizon Navigation for MAVs'.
Preview
Content: Published Version
Filetype: PDF
Size: 1MB