  Global navigation satellite systems (GNSS) denied environments/conditions
require unmanned aerial vehicles (UAVs) to energy-efficiently and reliably fly.
To this end, this study presents perception-and-energy-aware motion planning
for UAVs in GNSS-denied environments. The proposed planner solves the
trajectory planning problem by optimizing a cost function consisting of two
indices: the total energy consumption of a UAV and the perception quality of
light detection and ranging (LiDAR) sensor mounted on the UAV. Before online
navigation, a high-fidelity simulator acquires a flight dataset to learn energy
consumption for the UAV and heteroscedastic uncertainty associated with LiDAR
measurements, both as functions of the horizontal velocity of the UAV. The
learned models enable the online planner to estimate energy consumption and
perception quality, reducing UAV battery usage and localization errors.
Simulation experiments in a photorealistic environment confirm that the
proposed planner can address the trade-off between energy efficiency and
perception quality under heteroscedastic uncertainty. The open-source code is
released at https://gitlab.com/ReI08/perception-energy-planner.
