  Recent advances in Unmanned Aerial Vehicles (UAVs) have resulted in their
quick adoption for wide a range of civilian applications, including precision
agriculture, biosecurity, disaster monitoring and surveillance. UAVs offer
low-cost platforms with flexible hardware configurations, as well as an
increasing number of autonomous capabilities, including take-off, landing,
object tracking and obstacle avoidance. However, little attention has been paid
to how UAVs deal with object detection uncertainties caused by false readings
from vision-based detectors, data noise, vibrations, and occlusion. In most
situations, the relevance and understanding of these detections are delegated
to human operators, as many UAVs have limited cognition power to interact
autonomously with the environment. This paper presents a framework for
autonomous navigation under uncertainty in outdoor scenarios for small UAVs
using a probabilistic-based motion planner. The framework is evaluated with
real flight tests using a sub 2 kg quadrotor UAV and illustrated in victim
finding Search and Rescue (SAR) case study in a forest/bushland. The navigation
problem is modelled using a Partially Observable Markov Decision Process
(POMDP), and solved in real time onboard the small UAV using Augmented Belief
Trees (ABT) and the TAPIR toolkit. Results from experiments using colour and
thermal imagery show that the proposed motion planner provides accurate victim
localisation coordinates, as the UAV has the flexibility to interact with the
environment and obtain clearer visualisations of any potential victims compared
to the baseline motion planner. Incorporating this system allows optimised UAV
surveillance operations by diminishing false positive readings from
vision-based object detectors.
