Detection and Tracking of Small Objects in Sparse 3D Laser Range Data

TL;DR

This paper introduces a real-time, efficient method for detecting and tracking small objects in sparse 3D laser data, suitable for MAVs with limited hardware, using a novel segmentation approach and evaluation on real and simulated data.

Contribution

A novel segmentation and tracking pipeline optimized for lightweight sensors and hardware, enabling real-time small object detection in sparse 3D point clouds.

Findings

Achieves real-time performance on MAV hardware.

Comparable results to state-of-the-art methods.

Effective filtering and mapping of dynamic and static data.

Abstract

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.