The paper introduces CMax-SLAM, a pioneering system that leverages event cameras for rotational motion estimation. It compares various front-end methods and proposes a novel back-end solution through bundle adjustment. The experiments cover synthetic and real-world datasets, showcasing the system's performance in challenging scenarios.
The study addresses the limitations of existing rotational motion estimation methods with event cameras. It introduces a systematic comparative analysis and presents a new solution with promising results. The proposed CMax-SLAM system demonstrates enhanced accuracy and robustness in both synthetic and real-world environments.
Key points include the comparison of front-end methods like PF-SMT, EKF-SMT, RTPT, CMax-GAE, and CMax-ω. The paper also evaluates the performance of BA using linear and cubic spline models. Results show that CMax-SLAM outperforms existing methods in terms of absolute and relative errors on synthetic datasets.
Overall, the research contributes to advancing event-based ego-motion estimation by introducing an innovative system that combines Contrast Maximization with bundle adjustment for improved trajectory refinement.
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by Shuang Guo,G... lúc arxiv.org 03-14-2024
https://arxiv.org/pdf/2403.08119.pdfYêu cầu sâu hơn