Visual Saliency-Aware Receding Horizon Autonomous Exploration with Application to Aerial Robotics

This paper presents a novel strategy for autonomous visual saliency-aware receding horizon exploration of unknown environments using aerial robots. Through a model of visual attention, incrementally built maps are annotated regarding the visual importance and saliency of different objects and entities in the environment. Provided this information, a path planner that simultaneously optimizes for exploration of unknown space, and also directs the robot's attention to focus on the most salient objects, is developed. Following a two-step optimization paradigm, the algorithm first samples a random tree and identifies the branch maximizing for new volume to be explored. The first viewpoint of this path is then provided as a reference to the second planning step. Within that, a new tree is spanned, admissible branches arriving at the reference viewpoint while respecting a time budget dependent on the robot endurance and its environment exploration rate are found and evaluated in terms of reobserving salient regions at sufficient resolution. The best branch is then selected and executed by the robot, and the whole process is iteratively repeated. The proposed method is evaluated regarding its ability to provide increased attention toward salient objects, is verified to run onboard a small aerial robot, and is demonstrated in a set of challenging experimental studies.