SPOT: Spatio-Temporal Obstacle-free Trajectory Planning for UAVs in an Unknown Dynamic Environment

TL;DR

This paper presents SPOT, a reactive, mapless trajectory planning framework for UAVs that uses vision-based perception and spatio-temporal planning to avoid obstacles in unknown, dynamic environments.

Contribution

The paper introduces a novel mapless, vision-based spatio-temporal planner with a backup module for robust UAV navigation in dynamic, unknown settings.

Findings

Outperforms state-of-the-art methods in dynamic obstacle avoidance

Demonstrates real-time operation in hardware experiments

Reduces computational overhead compared to map-based approaches

Abstract

We address the problem of reactive motion planning for quadrotors operating in unknown environments with dynamic obstacles. Our approach leverages a 4-dimensional spatio-temporal planner, integrated with vision-based Safe Flight Corridor (SFC) generation and trajectory optimization. Unlike prior methods that rely on map fusion, our framework is mapless, enabling collision avoidance directly from perception while reducing computational overhead. Dynamic obstacles are detected and tracked using a vision-based object segmentation and tracking pipeline, allowing robust classification of static versus dynamic elements in the scene. To further enhance robustness, we introduce a backup planning module that reactively avoids dynamic obstacles when no direct path to the goal is available, mitigating the risk of collisions during deadlock situations. We validate our method extensively in both simulation and real-world hardware experiments, and benchmark it against state-of-the-art approaches, showing significant advantages for reactive UAV navigation in dynamic, unknown environments.