LiDARFlow: Real-Time Panel-Based MAV Guidance in Unknown Environments

2026-10-01

AI Summary

This study addresses the challenge of enabling micro aerial vehicles to perform real-time obstacle avoidance and navigation in unknown, complex environments relying solely on onboard perception. The proposed method extends the panel method from aerodynamic potential flow theory to unknown settings by constructing an online obstacle representation field from LiDAR point clouds. This representation generates smooth, collision-free guidance vectors that are integrated with a nominal vector field for flight control. The primary contribution is a lightweight, purely onboard framework for real-time obstacle representation and guidance. Indoor experiments validate its fully real-time obstacle avoidance capabilities in both waypoint and heading navigation tasks. With low computational overhead, the approach is well suited for deployment on resource-constrained aerial platforms.

Abstract

This paper presents a guidance algorithm for micro aerial vehicles operating in unknown, cluttered environments using only onboard sensing. The method is based on a panel formulation originally derived from aerodynamic potential-flow theory and generates smooth, collision-free guidance vectors from locally perceived obstacles. The approach is extended to unknown environments by constructing and updating the obstacle representation online from onboard LiDAR measurements. The resulting obstacle-avoidance field is integrated with a nominal guiding vector field to produce the final control input. The system is experimentally validated in indoor flight tests under two scenarios: waypoint navigation and directional guidance. In both cases, the vehicle successfully completes its task while avoiding all obstacles in real time using only onboard perception. The results demonstrate that the method is computationally lightweight and suitable for onboard implementation, with pointcloud processing identified as the main practical limitation. These results support the feasibility of lightweight onboard guidance in unknown environments.

Problem

• Micro Aerial Vehicle
• Obstacle Avoidance
• Unknown Environments
• Onboard Guidance
• LiDAR

Innovation

• LiDAR-based guidance
• Panel method
• Micro aerial vehicles
• Obstacle avoidance
• Unknown environments