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Moving event detection from LiDAR point streams.
Wu, Huajie; Li, Yihang; Xu, Wei; Kong, Fanze; Zhang, Fu.
Afiliación
  • Wu H; Department of Mechanical Engineering, The University of Hong Kong, Pokfulam, Hong Kong, 999077, China.
  • Li Y; Department of Mechanical Engineering, The University of Hong Kong, Pokfulam, Hong Kong, 999077, China.
  • Xu W; Department of Mechanical Engineering, The University of Hong Kong, Pokfulam, Hong Kong, 999077, China.
  • Kong F; Department of Mechanical Engineering, The University of Hong Kong, Pokfulam, Hong Kong, 999077, China.
  • Zhang F; Department of Mechanical Engineering, The University of Hong Kong, Pokfulam, Hong Kong, 999077, China. fuzhang@hku.hk.
Nat Commun ; 15(1): 345, 2024 Jan 06.
Article en En | MEDLINE | ID: mdl-38184659
ABSTRACT
In dynamic environments, robots require instantaneous detection of moving events with microseconds of latency. This task, known as moving event detection, is typically achieved using event cameras. While light detection and ranging (LiDAR) sensors are essential for robots due to their dense and accurate depth measurements, their use in event detection has not been thoroughly explored. Current approaches involve accumulating LiDAR points into frames and detecting object-level motions, resulting in a latency of tens to hundreds of milliseconds. We present a different approach called M-detector, which determines if a point is moving immediately after its arrival, resulting in a point-by-point detection with a latency of just several microseconds. M-detector is designed based on occlusion principles and can be used in different environments with various types of LiDAR sensors. Our experiments demonstrate the effectiveness of M-detector on various datasets and applications, showcasing its superior accuracy, computational efficiency, detection latency, and generalization ability.

Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2024 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2024 Tipo del documento: Article País de afiliación: China