  This paper presents PANTHER, a real-time perception-aware (PA) trajectory
planner in dynamic environments. PANTHER plans trajectories that avoid dynamic
obstacles while also keeping them in the sensor field of view (FOV) and
minimizing the blur to aid in object tracking. The rotation and translation of
the UAV are jointly optimized, which allows PANTHER to fully exploit the
differential flatness of multirotors. Real-time performance is achieved by
implicitly imposing this constraint through the Hopf fibration. PANTHER is able
to keep the obstacles inside the FOV 7.4 and 1.5 times more than non-PA
approaches and PA approaches that decouple translation and yaw, respectively.
The projected velocity (and hence the blur) is reduced by 30%. Our
recently-derived MINVO basis is used to impose low-conservative collision
avoidance constraints in position and velocity space. Finally, extensive
hardware experiments in unknown dynamic environments with all the computation
running onboard are presented, with velocities of up to 5.8 m/s, and with
relative velocities (with respect to the obstacles) of up to 6.3 m/s. The only
sensors used are an IMU, a forward-facing depth camera, and a downward-facing
monocular camera.
