arXiv:2606.25393cs.RO2026-06中稿 · manuscript

FLISP实现无人机与无人车协同巡检，无需地图即可高效避障

Large-Scale Tunnel Air-Ground Collaboration With FLISP: Fast LiDAR-IMU Synchronized Path Planner

同济大学智能自主系统研究所, 上海 201210, 中国同济大学自主智能无人系统全国重点实验室, 上海 201210, 中国同济大学电子与信息工程学院, 上海 201804, 中国作者：Fenghe Guo、Runjie Shen、Chenyang Sun、Junrui Zhang、Quanxi Zhan、Yongchun Wang、Junjie Zhang
论文配图：Large-Scale Tunnel Air-Ground Collaboration With FLISP: Fast LiDAR-IMU Synchronized Path Planner
图 1 · 摘自论文原文
  • 单套激光雷达惯导系统同步规划车机路径，省去建图环节
  • 实测1.2公里隧道成功率达100%，延迟仅7毫秒，提速7倍以上
  • 适合复杂狭窄环境如水电隧道的自动化巡检，可快速部署

水电隧道巡检对基础设施安全至关重要，但传统人工方式效率低且危险。本文提出FLISP（Fast LiDAR-IMU Synchronized Path Planner），一种无地图的车载-机协同巡检规划框架。不同于传统依赖地图的方法，FLISP具有三大创新：(1) 统一架构下，单一车载激光雷达-惯导套件同步生成车机路径；(2) 平台专用求解器，采用改进萤火虫算法实现无人车避障，动态迭代优化器保障无人机飞行；(3) 分层精修策略，在无状态估计漂移前提下保证运动学可行性。在1.2公里运行隧道中测试表明，FLISP规避了地图方法的结构瓶颈，消除地图栅格化开销（Fast-LIO2 + A*）和采样不稳定性（LIO-SAM + RRT*）。实现100%成功率，7毫秒延迟，较基于网格方法提速7倍，较基于采样的基线提升三个数量级。已在实际水电隧道验证，为特征退化的线性基础设施提供了可扩展的机器人巡检方案。演示视频见https://youtu.be/Y_ezs1PfLJ4，代码开源地址：https://github.com/ArchibaldGuo/FLISP.git。

原文摘要 · Abstract (English)

Hydropower tunnel inspection is critical for infrastructure integrity yet remains inefficient and hazardous using manual methods. We propose FLISP (Fast LiDAR-IMU Synchronized Path Planner), a mapless planning framework for cooperative UGV-UAV inspection. Unlike traditional map-based paradigms, FLISP features three core contributions: (1) a unified architecture where a single UGV-mounted LiDAR-IMU suite drives synchronized path generation for both platforms; (2) platform-specific solvers utilizing an enhanced Firefly Algorithm for UGV obstacle avoidance and a dynamic iterative optimizer for UAV flight; and (3) a hierarchical refinement strategy ensuring kinematic feasibility without state estimation drift. Benchmarks in a 1.2 km operational tunnel demonstrate that FLISP circumvents structural bottlenecks of map-based methods, eliminating map rasterization overhead (Fast-LIO2 + A*) and sampling instability (LIO-SAM + RRT*). FLISP achieves a 100% success rate with 7 ms latency, representing a 7-fold speedup over grid-based and a three-order-of-magnitude improvement over sampling-based baselines. Validated in operational hydropower tunnels, this approach offers a scalable solution for robotic inspection in feature-degraded linear infrastructure. A demonstration video is available at https://youtu.be/Y_ezs1PfLJ4, and the code at https://github.com/ArchibaldGuo/FLISP.git.

协同巡检无地图规划激光雷达无人机

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