Implicit Event-RGBD Neural SLAM

Delin Qu^1,2* Chi Yan^2,5* Dong Wang²

Jie Yin³ Qizhi Chen²

Dan Xu⁵ Yiting Zhang² Bin Zhao^2,4† Xuelong ^Li2

¹Fudan University ²Shanghai AI Laboratory ³Shanghai Jiao Tong University

⁴Northwestern Polytechnical University ⁵Hong Kong University of Sciences and Technology

Abstract

Implicit neural SLAM has achieved remarkable progress recently. Nevertheless, existing methods face significant challenges in non-ideal scenarios, such as motion blur or lighting variation, which often leads to issues like convergence failures, localization drifts, and distorted mapping. To address these challenges, we propose EN-SLAM, the first event-RGBD implicit neural SLAM framework, which effectively leverages the high rate and high dynamic range advantages of event data for tracking and mapping. Specifically, EN-SLAM proposes a differentiable CRF (Camera Response Function) rendering technique to generate distinct RGB and event camera data via a shared radiance field, which is optimized by learning a unified implicit representation with the captured event and RGBD supervision. Moreover, based on the temporal difference property of events, we propose a temporal aggregating optimization strategy for the event joint tracking and global bundle adjustment, capitalizing on the consecutive difference constraints of events, significantly enhancing tracking accuracy and robustness. Finally, we construct the simulated dataset DEV-Indoors and real captured dataset DEV-Reals containing 6 scenes, 17 sequences with practical motion blur and lighting changes for evaluations. Experimental results show that our method outperforms the SOTA methods in both tracking ATE and mapping ACC with a real-time 17 FPS in various challenging environments. Project page:

https://delinqu.github.io/EN-SLAM.