Efficient Moving Object Segmentation in LiDAR Point Clouds Using Minimal Number of Sweeps

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

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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.

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