NextStop: An Improved Tracker For Panoptic LIDAR Segmentation Data

TL;DR

NextStop is an advanced tracker for 4D panoptic LiDAR segmentation that improves temporal consistency and tracking accuracy, especially for small objects, by integrating motion estimation and lifespan management.

Contribution

It introduces NextStop, a novel tracker that combines Kalman filter-based motion estimation with lifespan management to enhance 4D LiDAR segmentation tracking performance.

Findings

Improved tracking accuracy for small objects like pedestrians and cyclists.

Fewer ID switches and earlier tracking initiation.

Enhanced reliability in complex environments.

Abstract

4D panoptic LiDAR segmentation is essential for scene understanding in autonomous driving and robotics, combining semantic and instance segmentation with temporal consistency. Current methods, like 4D-PLS and 4D-STOP, use a tracking-by-detection methodology, employing deep learning networks to perform semantic and instance segmentation on each frame. To maintain temporal consistency, large-size instances detected in the current frame are compared and associated with instances within a temporal window that includes the current and preceding frames. However, their reliance on short-term instance detection, lack of motion estimation, and exclusion of small-sized instances lead to frequent identity switches and reduced tracking performance. We address these issues with the NextStop1 tracker, which integrates Kalman filter-based motion estimation, data association, and lifespan management, along with a tracklet state concept to improve prioritization. Evaluated using the LiDAR Segmentation and Tracking Quality (LSTQ) metric on the SemanticKITTI validation set, NextStop demonstrated enhanced tracking performance, particularly for small-sized objects like people and bicyclists, with fewer ID switches, earlier tracking initiation, and improved reliability in complex environments. The source code is available at https://github.com/AIROTAU/NextStop