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

arXiv: https://arxiv.org/abs/1903.05889 · HTML: https://mlsanity.com/paper/1903.05889/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2019.

## Built on by (most cited first, 4 of 4)

- [3D-SiamRPN: An End-to-End Learning Method for Real-Time 3D Single Object Tracking Using Raw Point Cloud](https://mlsanity.com/paper/2108.05630/) (2021)
- [Unsupervised UAV 3D Trajectories Estimation with Sparse Point Clouds](https://mlsanity.com/paper/2412.12716/) (2024)
- [Detection and Tracking of MAVs Using a Rosette Scanning Pattern LiDAR](https://mlsanity.com/paper/2408.08555/) (2024)
- [Separating Drone Point Clouds From Complex Backgrounds by Cluster Filter -- Technical Report for CVPR 2024 UG2 Challenge](https://mlsanity.com/paper/2412.16947/) (2024)

## Abstract (arXiv)

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.