EGO-Planner
A lightweight gradient-based local planner for quadrotors that eliminates ESDF construction, achieving planning times around 1ms.
What is EGO-Planner?
EGO-Planner is a gradient-based local planner for quadrotors that avoids Euclidean Signed Distance Field (ESDF) construction to achieve extremely fast planning times. It solves the problem of computational bottlenecks in real-time drone navigation by providing a lightweight alternative to ESDF-dependent planners, with total planning times around 1ms.
Researchers and developers working on autonomous drone navigation, particularly those focused on real-time local planning for quadrotors in ROS-based systems.
Developers choose EGO-Planner for its elimination of ESDF computation—a major performance bottleneck—resulting in significantly faster planning times (≈1ms) compared to state-of-the-art methods, while maintaining trajectory quality and safety.
Overview
EGO-Planner is an ESDF-free gradient-based local planner designed for quadrotor navigation. It significantly reduces computation time compared to state-of-the-art methods by avoiding the computationally expensive Euclidean Signed Distance Field (ESDF) construction, enabling real-time performance with total planning times around 1ms.
Key Features
- ESDF-Free Planning — Eliminates the need to compute Euclidean Signed Distance Fields, drastically reducing computational overhead.
- Lightweight Gradient-Based Optimization — Uses a gradient-based approach for efficient local trajectory generation.
- GPU/CPU Versatility — Offers both GPU and CPU versions of its local sensing module for depth image generation or pointcloud processing.
- Fast Computation — Achieves planning times of approximately 1ms, suitable for real-time drone control.
- Simulation-Ready — Includes a lightweight quadrotor simulator and supports integration with sensors like Intel RealSense for hardware testing.
Philosophy
EGO-Planner prioritizes computational efficiency and real-time performance by removing the ESDF construction bottleneck, making advanced local planning accessible for resource-constrained aerial robotics applications.
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