Developing Flying Explorer for Autonomous Digital Modelling in Wild Unknowns

TL;DR

This study proposes an innovative solution for robot odometry, path planning, and exploration in unknown outdoor environments, with a focus on Digital modelling, using a minimum cost formulation with pseudo-randomly generated objectives, integrating multi-path planning and evaluation.

Abstract

This work presents an innovative solution for robotic odometry, path planning and exploration in wild unknown environments, focusing on digital modelling. The approach uses a minimum cost formulation with pseudo-randomly generated objectives, integrating multi-path planning and evaluation, with emphasis on full coverage of unknown maps based on feasible boundaries of interest. The evaluation carried out on a robotic platform with a lightweight 3D LiDAR sensor model, assesses the consistency and efficiency in exploring completely unknown subterranean-like areas. The algorithm allows for dynamic changes to the desired target and behaviour. At the same time, the paper details the design of AREX, highlighting its robust localisation, mapping and efficient exploration target selection capabilities, with a focus on continuity in exploration direction for increased efficiency and reduced odometry errors. The real-time, high-precision environmental perception module is identified as critical for accurate obstacle avoidance and exploration boundary identification.

Figures (10)

• Figure 1: AREX: Flight System Designed for High Durability, Multi-Scenario Exploration, and Environmental Modeling.
• Figure 2: Robot Design (a) AREX's frame structure and sensor layout. The MID-360 LiDAR is positioned at the front of the fuselage at a 45$^\circ$ angle to the horizontal z-axis, while the Realsense D430 is mounted above the MID-360 to fill in blind spots in the point cloud. The front dual arms of AREX form a 120$^\circ$ angle, maximizing the avoidance of obstructing the LIDAR's scanning range. (b) AREX's circuitry structure. Reflects the circuitry between the ESCs and motors, as well as the communication interfaces and protocols among various modules.
• Figure 3: AREX: Flight System Designed for High Durability, Multi-Scenario Exploration, and Environmental Modeling.
• Figure 4: A graphic illustration of the VINS-Fusion framework. If the latest keyframe comes, it will be kept, and the visual and IMU measurements of the oldest frame will be marginalized. The prior factor of the loss function is obtained from marginalization. We can get the IMU propogation factor from IMU pre-integration. By computing the reprojection error between two keyframes, we can get the vision factor.
• Figure 5: System overview of improved FAST-LIO, which can output high-frequency odometry.