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Multi-object tracking (MOT) has important applications in monitoring,logistics, and other fields. This paper develops a real-time multi-objecttracking and prediction system in rugged environments. A 3D object detectionalgorithm based on Lidar-camera fusion is designed to detect the targetobjects. Based on the Hungarian algorithm, this paper designs a 3D multi-objecttracking algorithm with an adaptive threshold to realize the stable matchingand 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-objecttracking to remove dynamic points for better SLAM performance and static map.To verify the effectiveness of the proposed multi-object tracking andprediction system, several simulations and physical experiments are conducted.The results show that the proposed system can track dynamic objects and providefuture trajectory and a more clean static map in real-time.

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