Uploaded May 2018 | Updated September 2026, 3 weeks ago
ICRA 2018 Spotlight Video
Interactive Session Tue PM Pod V.2
Authors: Zhang, Zichao; Scaramuzza, Davide
Title: Perception-aware Receding Horizon Navigation for MAVs
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 forward-looking 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).
ICRA 2018 Spotlight Video
Interactive Session Tue PM Pod V.2
Authors: Zhang, Zichao; Scaramuzza, Davide
Title: Perception-aware Receding Horizon Navigation for MAVs
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 forward-looking 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).
