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Wanli Xing 0002

dblp:115/7574-2 · DBLP profile ↗
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2ranked-venue papers
2as first author
2since 2021 · last 2026
0009-0005-9515-214XORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Robot navigation and mapping · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping
SLAM
1.012026
EROAM: Event-Based Camera Rotational Odometry and Mapping in Real Time · IEEE Trans. Robotics 2026
Robotics › Robot navigation and mapping
visual odometry
1.012026
EROAM: Event-Based Camera Rotational Odometry and Mapping in Real Time · IEEE Trans. Robotics 2026

Methods — techniques the papers use, named apart from their topics

real-time odometry · 1.0event-based camera · 1.0
YearPublicationVenuePosition
2026 EROAM: Event-Based Camera Rotational Odometry and Mapping in Real Time
Wanli Xing 0002, Shijie Lin, Linhan Yang, Zeqing Zhang, Yanjun Du, Maolin Lei, Yipeng Pan, Chen Wang 0123, Jia Pan 0001
IEEE Trans. Robotics1
2025 EventSync: Joint Recovery of Temporal Offsets and Relative Orientations for Wide-Baseline Event Cameras
abstract
Event-Based cameras offer significant advantages due to their high temporal resolution and low power consumption. However, when deploying multiple such cameras, a critical challenge emerges: each camera operates on an independent time system, resulting in temporal misalignment that severely degrades performance in multi-event camera applications. Traditional hardware-based synchronization methods face significant limitations in compatibility and are impractical for wide-baseline configurations. We introduce EventSync, a software-based algorithm that achieves millisecond-level synchronization by exploiting the motion of objects in the cameras’ shared field of view, while simultaneously estimating the relative orientation between cameras. Our approach eliminates the need for physical connections, making it particularly valuable for wide-baseline deployments. Through comprehensive evaluation in both simulated environments and real-world indoor/outdoor scenarios, we demonstrate robust synchronization accuracy and precise extrinsic calibration across varying camera configurations, significantly outperforming existing methods. Code: https://github.com/wlxing1901/event-sync
Wanli Xing 0002, Shijie Lin, Guangze Zheng 0001, Linhan Yang, Yanjun Du, Jia Pan 0001
IROS1