GRE-SLAM: 6-DoF Pure Event-Based SLAM with Semi-Dense Depth Recovery Assisted Bundle Adjustment

Yang Chen, Lin Zhang

Abstract: Event cameras are innovative bioinspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. Such cameras do not suffer from motion blur and cope well with scenes characterized by high dynamic range, which can benefit classic computer vision tasks such as pose estimation. However, currently developed event-based pose estimation methods either require extra data as inputs (such as IMU data or depths) or lack a global refinement step to alleviate accumulated drifts. To this end, we propose the first 6-DoF pure event-based SLAM system equipped with back-end global optimization, named GRE-SLAM (Globally Refined Event-based SLAM). For robustness and accuracy, first, 6-DoF motion compensation is introduced in the front-end to prepare sharp-edged event frames and a favorable initialization pose, mitigating unstable optimization during event registration brought by sparsity and noise of events. Second, a novel adaptive semi-dense depth recovery algorithm enriches front-end's sparse depths without additional sensors, helping establish long-term edge alignment constraints to support global BA in the back-end. Comprehensive experiments on real-world datasets demonstrate that our method can produce high-accuracy pose estimation results as well as recover a semi-dense depth map for each Image of Warped Events (IWE).

External IDs:dblp:conf/mir/Chen025