Rózsa, Zoltán and Madaras, Ákos and Szirányi, Tamás (2025) Efficient Moving Object Segmentation in LiDAR Point Clouds Using Minimal Number of Sweeps. IEEE OPEN JOURNAL OF SIGNAL PROCESSING, 6. pp. 118-128. ISSN 2644-1322 10.1109/OJSP.2025.3532199

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Abstract

LiDAR point clouds are a rich source of information for autonomous vehicles and ADAS systems. However, they can be challenging to segment for moving objects as - among other things - finding correspondences between sparse point clouds of consecutive frames is difficult. Traditional methods rely on a (global or local) map of the environment, which can be demanding to acquire and maintain in real-world conditions and the presence of the moving objects themselves. This paper proposes a novel approach using as minimal sweeps as possible to decrease the computational burden and achieve mapless moving object segmentation (MOS) in LiDAR point clouds. Our approach is based on a multimodal learning model with single-modal inference. The model is trained on a dataset of LiDAR point clouds and related camera images. The model learns to associate features from the two modalities, allowing it to predict dynamic objects even in the absence of a map and the camera modality. We propose semantic information usage for multi-frame instance segmentation in order to enhance performance measures. We evaluate our approach to the SemanticKITTI and Apollo real-world autonomous driving datasets. Our results show that our approach can achieve state-of-the-art performance on moving object segmentation and utilize only a few (even one) LiDAR frames. The implementation with examples and pre-trained networks is available: https://github.com/madak88/2DPASS-MOS © 2020 IEEE.

Item Type:	Article
Uncontrolled Keywords:	LIDAR; image enhancement; Real-world; Inference engines; Moving objects; Knowledge transfer; Magnetic levitation vehicles; Autonomous Vehicles; POINT CLOUDS; autonomous driving; Semantic segmentation; Sources of informations; moving object segmentation; sparse point cloud; Point-clouds;
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Distributed Events Analysis Research Laboratory
SWORD Depositor:	MTMT Injector
Depositing User:	MTMT Injector
Date Deposited:	25 Feb 2025 12:09
Last Modified:	25 Feb 2025 12:09
URI:	https://eprints.sztaki.hu/id/eprint/10870

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