一种搭载视觉惯性编码器里程计与基于强化学习的主动SLAM方法的新型ViDAR装置
A Novel ViDAR Device With Visual Inertial Encoder Odometry and Reinforcement Learning-Based Active SLAM Method
AI总结：
本研究提出基于ViDAR装置的视觉惯性编码器紧耦合里程计与DRL驱动的平台运动解耦主动SLAM方法，通过新增电机编码器提升帧间共视与状态估计精度，为主动SLAM系统设计提供新思路。
摘要：
在多传感器融合同步定位与建图（SLAM）领域，单目相机与IMU被广泛用于构建简单有效的视觉惯性系统。然而，针对集成电机编码器装置以提升SLAM性能的研究较为有限。引入此类装置后，有望以极低的额外成本与结构复杂度，显著提升主动能力与视场（FOV）。本文提出一种基于ViDAR（视频探测与测距）装置的新型视觉惯性编码器紧耦合里程计（VIEO），并提出了一种ViDAR标定方法以保障VIEO的准确初始化。此外，本文还提出一种基于深度强化学习（DRL）的平台运动解耦主动SLAM方法。实验数据表明，所提出的ViDAR与VIEO算法相较于对应的视觉惯性里程计（VIO）算法，显著增加了帧间共视关系，提升了状态估计精度。同时，具备平台运动解耦能力的基于DRL的主动SLAM算法，能够提升特征点的多样性权重，进一步增强VIEO算法的性能。所提出的方法为复杂环境下主动SLAM系统的平台设计更新与解耦方法研究提供了新的思路。
英文摘要：
In the field of multi-sensor fusion for simultaneous localization and mapping (SLAM), monocular cameras and IMUs are widely used to build simple and effective visual-inertial systems. However, limited research has explored the integration of motor-encoder devices to enhance SLAM performance. By incorporating such devices, it is possible to significantly improve active capability and field of view (FOV) with minimal additional cost and structural complexity. This paper proposes a novel visual-inertial-encoder tightly coupled odometry (VIEO) based on a ViDAR (Video Detection and Ranging) device. A ViDAR calibration method is introduced to ensure accurate initialization for VIEO. In addition, a platform motion decoupled active SLAM method based on deep reinforcement learning (DRL) is proposed. Experimental data demonstrate that the proposed ViDAR and the VIEO algorithm significantly increase cross-frame co-visibility relationships compared to its corresponding visual-inertial odometry (VIO) algorithm, improving state estimation accuracy. Additionally, the DRL-based active SLAM algorithm, with the ability to decouple from platform motion, can increase the diversity weight of the feature points and further enhance the VIEO algorithm's performance. The proposed methodology sheds fresh insights into both the updated platform design and decoupled approach of active SLAM systems in complex environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
