Multi-object Detection, Tracking and Prediction in Rugged Dynamic Environments

TL;DR

This paper presents a real-time multi-object detection, tracking, and prediction system in rugged environments using Lidar-camera fusion, adaptive algorithms, and neural networks, improving dynamic object tracking and static map quality.

Contribution

It introduces a novel multi-object tracking and prediction system combining Lidar-camera fusion, adaptive Hungarian algorithm, and neural networks for rugged terrains.

Findings

Effective real-time tracking of dynamic objects

Accurate 3D trajectory prediction in rugged environments

Enhanced static map quality by removing dynamic points

Abstract

Multi-object tracking (MOT) has important applications in monitoring, logistics, and other fields. This paper develops a real-time multi-object tracking and prediction system in rugged environments. A 3D object detection algorithm based on Lidar-camera fusion is designed to detect the target objects. Based on the Hungarian algorithm, this paper designs a 3D multi-object tracking algorithm with an adaptive threshold to realize the stable matching and tracking of the objects. We combine Memory Augmented Neural Networks (MANN) and Kalman filter to achieve 3D trajectory prediction on rugged terrains. Besides, we realize a new dynamic SLAM by using the results of multi-object tracking to remove dynamic points for better SLAM performance and static map. To verify the effectiveness of the proposed multi-object tracking and prediction system, several simulations and physical experiments are conducted. The results show that the proposed system can track dynamic objects and provide future trajectory and a more clean static map in real-time.