LiDAR-based Quadrotor Autonomous Inspection System in Cluttered Environments

2025-03-29

AI Summary

To address the limitations of GNSS-denied environments, complex unstructured terrain, and heavy reliance on manual operation in UAV inspection, this paper proposes a LiDAR-driven quadrotor autonomous inspection system. Methodologically, it introduces a novel two-stage paradigm—“human-in-the-loop mapping followed by fully autonomous execution”—integrating 3D LiDAR SLAM for robust pose estimation, ESDF-based real-time collision avoidance, TSP-optimized inspection point sequencing, and a lightweight navigation control framework. Key contributions include: (1) advancing robust pose estimation and task-oriented path planning under GNSS-denied conditions; and (2) enabling rapid deployment by non-expert users for high-precision inspection across industrial, geological, and agricultural scenarios. Experimental results demonstrate a 40% reduction in trajectory length and a 57% decrease in flight time compared to manual operation, with stable performance across diverse cluttered environments—including sloped terrain, landslide areas, farmland, factories, and forests.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

In recent years, autonomous unmanned aerial vehicle (UAV) technology has seen rapid advancements, significantly improving operational efficiency and mitigating risks associated with manual tasks in domains such as industrial inspection, agricultural monitoring, and search-and-rescue missions. Despite these developments, existing UAV inspection systems encounter two critical challenges: limited reliability in complex, unstructured, and GNSS-denied environments, and a pronounced dependency on skilled operators. To overcome these limitations, this study presents a LiDAR-based UAV inspection system employing a dual-phase workflow: human-in-the-loop inspection and autonomous inspection. During the human-in-the-loop phase, untrained pilots are supported by autonomous obstacle avoidance, enabling them to generate 3D maps, specify inspection points, and schedule tasks. Inspection points are then optimized using the Traveling Salesman Problem (TSP) to create efficient task sequences. In the autonomous phase, the quadrotor autonomously executes the planned tasks, ensuring safe and efficient data acquisition. Comprehensive field experiments conducted in various environments, including slopes, landslides, agricultural fields, factories, and forests, confirm the system's reliability and flexibility. Results reveal significant enhancements in inspection efficiency, with autonomous operations reducing trajectory length by up to 40% and flight time by 57% compared to human-in-the-loop operations. These findings underscore the potential of the proposed system to enhance UAV-based inspections in safety-critical and resource-constrained scenarios.

Problem

• Enhancing UAV reliability in complex GNSS-denied environments
• Reducing dependency on skilled operators for inspections
• Optimizing autonomous inspection efficiency using LiDAR and TSP

Innovation

• LiDAR-based UAV with dual-phase workflow
• Autonomous obstacle avoidance for untrained pilots
• TSP-optimized inspection points for efficiency