用编码器+视觉惯性融合提升机器人定位精度与视野。
A Novel ViDAR Device With Visual Inertial Encoder Odometry and Reinforcement Learning-Based Active SLAM Method
- 引入编码器增强视觉惯性系统，实现紧耦合里程计
- 实验显示跨帧可见性提升，状态估计更准确
- 基于强化学习的主动建图可解耦平台运动，适合复杂环境
在多传感器融合的同步定位与建图（SLAM）领域，单目相机与惯性测量单元（IMU）被广泛用于构建简单高效的视觉惯性系统。然而，关于电机编码器设备集成以提升SLAM性能的研究仍有限。通过引入此类设备，可在几乎不增加成本与结构复杂度的前提下显著提升系统的主动能力与视场（FOV）。本文提出一种基于ViDAR（Video Detection and Ranging）设备的新型视觉-惯性-编码器紧耦合里程计（VIEO），并设计了相应的ViDAR标定方法以确保初始精度。此外，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。实验表明，所提ViDAR设备与VIEO算法相比其对应的视觉惯性里程计（VIO）算法，显著提升了跨帧共可见性关系，从而提高了状态估计精度。同时，基于DRL的主动SLAM算法具备解耦平台运动的能力，可增加特征点多样性权重，进一步优化VIEO性能。该方法为复杂环境下的主动SLAM系统提供了新的平台设计思路与解耦范式。
原文摘要 · Abstract (English)
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.
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