  Robotic mapping is attractive in many scientific applications that involve
environmental surveys. This paper presents a system for localization and
mapping of sparsely distributed surface features such as precariously balanced
rocks (PBRs), whose geometric fragility parameters provide valuable information
on earthquake processes and landscape development. With this geomorphologic
problem as the test domain, we carry out a lawn-mower search pattern from a
high elevation using an Unpiloted Aerial Vehicle (UAV) equipped with a flight
controller, GPS module, stereo camera, and onboard computer. Once a potential
PBR target is detected by a deep neural network in real time, we track its
bounding box in the image coordinates by applying a Kalman filter that fuses
the deep learning detection with Kanade-Lucas-Tomasi (KLT) tracking. The target
is localized in world coordinates using depth filtering where a set of 3D
points are filtered by object bounding boxes from different camera
perspectives. The 3D points also provide a strong prior on target shape, which
is used for UAV path planning to closely map the target using RGBD SLAM. After
target mapping, the UAS resumes the lawn-mower search pattern to locate and map
the next target.
