VAULT融合多传感器实现机器人室内外高精度实时定位
VAULT: A Mobile Mapping System for ROS 2-based Autonomous Robots
- 用GNSS、VIO、IMU与EKF融合生成可靠3D位姿
- 结合VSLAM构建完整3D点云地图，提升定位精度
- 专为农业林业等户外场景设计，适合ROS 2机器人使用
定位是自主机器人导航能力的关键。虽然室内环境可依赖轮式里程计和2D LiDAR建图，但农业、林业等室外场景面临独特挑战，需实现实时定位与一致建图。本文提出VAULT原型系统——一个基于ROS 2的移动测绘系统（MMS），融合多种传感器以实现鲁棒的室内外定位。该方案利用全球导航卫星系统（GNSS）数据、视觉惯性里程计（VIO）、惯性测量单元（IMU）数据及扩展卡尔曼滤波（EKF），生成可靠的3D里程计。为进一步提升定位精度，引入视觉同步定位与建图（VSLAM），生成完整的3D点云地图。通过整合这些传感器技术与先进算法，该原型为自主移动机器人提供了全面的室外定位解决方案，使其能够自信且精确地导航与测绘周围环境。
原文摘要 · Abstract (English)
Localization plays a crucial role in the navigation capabilities of autonomous robots, and while indoor environments can rely on wheel odometry and 2D LiDAR-based mapping, outdoor settings such as agriculture and forestry, present unique challenges that necessitate real-time localization and consistent mapping. Addressing this need, this paper introduces the VAULT prototype, a ROS 2-based mobile mapping system (MMS) that combines various sensors to enable robust outdoor and indoor localization. The proposed solution harnesses the power of Global Navigation Satellite System (GNSS) data, visual-inertial odometry (VIO), inertial measurement unit (IMU) data, and the Extended Kalman Filter (EKF) to generate reliable 3D odometry. To further enhance the localization accuracy, Visual SLAM (VSLAM) is employed, resulting in the creation of a comprehensive 3D point cloud map. By leveraging these sensor technologies and advanced algorithms, the prototype offers a comprehensive solution for outdoor localization in autonomous mobile robots, enabling them to navigate and map their surroundings with confidence and precision.
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