Event Camera Meets Mobile Embodied Perception: Abstraction, Algorithm, Acceleration, Application

With the evolution of mobile embodied intelligence, agents such as drones and autonomous robots are transitioning toward high agility. This shift imposes stringent demands on embodied perception, requiring high-accuracy and low-latency feedback loops for reliable interaction. Event-based vision has emerged as a transformative paradigm. Its microsecond-level temporal resolution and high dynamic range render it ideal for embodied perception tasks on high-agility mobile platforms. However, asynchronous nature, substantial noise, lack of persistent semantic information, and large data volume pose challenges for processing on resource-constrained mobile agents. This article surveys the literature from 2014–2025 and presents a comprehensive overview of event-based mobile embodied perception. We organize review around four key pillars: event abstraction methods, perception algorithm advancements, hardware and software acceleration strategies, and mobile applications . We discuss critical tasks including visual odometry, object tracking, optical flow, and 3D reconstruction, while highlighting challenges associated with sensor fusion and real-time deployment. Furthermore, we outline future research directions, such as improving event cameras with advanced optics and leveraging neuromorphic computing for efficient processing. To support ongoing research, we provide an open-source Online Sheet with recent developments. We hope this survey serves as a reference, facilitating adoption of event-based vision across diverse mobile embodied applications.

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Publication Details

Journal
ACM Computing Surveys
Published
2025-12-25
DOI
https://doi.org/10.1145/3786332
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73
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Event Camera Meets Mobile Embodied Perception: Abstraction, Algorithm, Acceleration, Application

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ACM Computing Surveys
Advanced Memory and Neural Computing
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article

Event Camera Meets Mobile Embodied Perception: Abstraction, Algorithm, Acceleration, Application

Xinlei Chen, Xinyu Luo, Haoyang Wang, Wenhua Ding, Ciyu Ruan, Jiayi Xu, Ruishan Guo, Pengtao Ma, Yunhao Liu, Tianyang Zhong
article en
1 citations

Abstract

With the evolution of mobile embodied intelligence, agents such as drones and autonomous robots are transitioning toward high agility. This shift imposes stringent demands on embodied perception, requiring high-accuracy and low-latency feedback loops for reliable interaction. Event-based vision has emerged as a transformative paradigm. Its microsecond-level temporal resolution and high dynamic range render it ideal for embodied perception tasks on high-agility mobile platforms. However, asynchronous nature, substantial noise, lack of persistent semantic information, and large data volume pose challenges for processing on resource-constrained mobile agents. This article surveys the literature from 2014–2025 and presents a comprehensive overview of event-based mobile embodied perception. We organize review around four key pillars: event abstraction methods, perception algorithm advancements, hardware and software acceleration strategies, and mobile applications . We discuss critical tasks including visual odometry, object tracking, optical flow, and 3D reconstruction, while highlighting challenges associated with sensor fusion and real-time deployment. Furthermore, we outline future research directions, such as improving event cameras with advanced optics and leveraging neuromorphic computing for efficient processing. To support ongoing research, we provide an open-source Online Sheet with recent developments. We hope this survey serves as a reference, facilitating adoption of event-based vision across diverse mobile embodied applications.

ACM Computing SurveysVol. 58(8)
University Town of Shenzhen (CN), Tsinghua–Berkeley Shenzhen Institute (CN), University of Hong Kong (HK), Tsinghua University (CN)
Openalex Percentile: Top 20%
Advanced Memory and Neural Computing
0.73
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