Detection and Tracking of Small Objects in Sparse 3D Laser Range Data
This addresses the challenge of enabling autonomous navigation for micro aerial vehicles in changing environments, though it appears incremental in its technical approach.
The paper tackles the problem of detecting and tracking small dynamic objects in sparse 3D laser range data from lightweight sensors on micro aerial vehicles, achieving real-time performance on limited hardware with results comparable to state-of-the-art methods.
Detection and tracking of dynamic objects is a key feature for autonomous behavior in a continuously changing environment. With the increasing popularity and capability of micro aerial vehicles (MAVs) efficient algorithms have to be utilized to enable multi object tracking on limited hardware and data provided by lightweight sensors. We present a novel segmentation approach based on a combination of median filters and an efficient pipeline for detection and tracking of small objects within sparse point clouds generated by a Velodyne VLP-16 sensor. We achieve real-time performance on a single core of our MAV hardware by exploiting the inherent structure of the data. Our approach is evaluated on simulated and real scans of in- and outdoor environments, obtaining results comparable to the state of the art. Additionally, we provide an application for filtering the dynamic and mapping the static part of the data, generating further insights into the performance of the pipeline on unlabeled data.