Splatblox: Traversability-Aware Gaussian Splatting for Outdoor Robot Navigation

TL;DR

Splatblox is a real-time outdoor robot navigation system that fuses RGB images and LiDAR data using Gaussian Splatting to create a traversability-aware ESDF, enabling effective navigation in complex terrains.

Contribution

It introduces a novel Gaussian Splatting-based method for online construction of a traversability-aware ESDF combining geometry and semantics for outdoor navigation.

Findings

Outperforms state-of-the-art methods with over 50% higher success rate.

Reduces freezing incidents by 40%.

Achieves up to 13% faster time to goal.

Abstract

We present Splatblox, a real-time system for autonomous navigation in outdoor environments with dense vegetation, irregular obstacles, and complex terrain. Our method fuses segmented RGB images and LiDAR point clouds using Gaussian Splatting to construct a traversability-aware Euclidean Signed Distance Field (ESDF) that jointly encodes geometry and semantics. Updated online, this field enables semantic reasoning to distinguish traversable vegetation (e.g., tall grass) from rigid obstacles (e.g., trees), while LiDAR ensures 360-degree geometric coverage for extended planning horizons. We validate Splatblox on a quadruped robot and demonstrate transfer to a wheeled platform. In field trials across vegetation-rich scenarios, it outperforms state-of-the-art methods with over 50% higher success rate, 40% fewer freezing incidents, 5% shorter paths, and up to 13% faster time to goal, while supporting long-range missions up to 100 meters. Experiment videos and more details can be found on our project page: https://splatblox.github.io