VAULT: A Mobile Mapping System for ROS 2-based Autonomous Robots

TL;DR

This paper presents VAULT, a ROS 2-based mobile mapping system that integrates GNSS, VIO, IMU, and EKF with Visual SLAM to enable accurate indoor and outdoor localization and mapping for autonomous robots.

Contribution

The paper introduces VAULT, a novel mobile mapping system that combines multiple sensors and algorithms within ROS 2 for improved localization and mapping in diverse environments.

Findings

Successful integration of GNSS, VIO, IMU, and EKF for reliable 3D odometry.

Enhanced localization accuracy through Visual SLAM and comprehensive 3D mapping.

Applicable to outdoor and indoor environments, demonstrating robustness and versatility.

Abstract

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.