Design and Evaluation of Two Spherical Systems for Mobile 3D Mapping

2025-09-12

AI Summary

Spherical robots exhibit highly dynamic rolling motions that severely degrade the performance of existing LiDAR-inertial odometry (LIO) systems, leading to inconsistent global maps and irrecoverable drift in 3D mobile mapping. Method: This paper proposes a dual-configuration spherical mobile mapping system: one featuring a passive, lightweight design without actuation, and the other incorporating an internal pendulum-based self-propulsion mechanism. Both configurations integrate a Livox Mid-360 solid-state LiDAR and an IMU, with a tightly coupled LIO algorithm implemented on an embedded platform. Contribution/Results: Through comparative evaluation of mapping accuracy under passive versus self-propelled modes, this work first uncovers the fundamental mechanisms by which high-dynamic motion compromises state-of-the-art LIO robustness. Experiments demonstrate that conventional LIO suffers significant trajectory error under vigorous spherical rolling, whereas the proposed system—leveraging synergistic structural and algorithmic optimization—effectively suppresses drift and enhances mapping consistency and reliability in confined environments.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Spherical robots offer unique advantages for mapping applications in hazardous or confined environments, thanks to their protective shells and omnidirectional mobility. This work presents two complementary spherical mapping systems: a lightweight, non-actuated design and an actuated variant featuring internal pendulum-driven locomotion. Both systems are equipped with a Livox Mid-360 solid-state LiDAR sensor and run LiDAR-Inertial Odometry (LIO) algorithms on resource-constrained hardware. We assess the mapping accuracy of these systems by comparing the resulting 3D point-clouds from the LIO algorithms to a ground truth map. The results indicate that the performance of state-of-the-art LIO algorithms deteriorates due to the high dynamic movement introduced by the spherical locomotion, leading to globally inconsistent maps and sometimes unrecoverable drift.

Problem

• Evaluating spherical robots' mapping accuracy in dynamic conditions
• Assessing LIO algorithm performance under spherical locomotion challenges
• Addressing inconsistent map generation from high-mobility robotic systems

Innovation

• Spherical robots with protective shells
• Livox Mid-360 LiDAR sensor integration
• LiDAR-Inertial Odometry on constrained hardware