Real-Time Metric-Semantic Mapping for Autonomous Navigation in Outdoor Environments

TL;DR

This paper presents a real-time, GPU-accelerated metric-semantic mapping system using LiDAR, Visual, and Inertial sensors for outdoor autonomous navigation, achieving high speed and integration with navigation tasks.

Contribution

The paper introduces an online, GPU-accelerated metric-semantic mapping system that fuses multi-modal sensor data for large-scale outdoor environments, enabling real-time mapping and navigation.

Findings

Frame processing time less than 7ms across scenarios

Effective large-scale outdoor environment mapping

Successful integration into autonomous navigation system

Abstract

The creation of a metric-semantic map, which encodes human-prior knowledge, represents a high-level abstraction of environments. However, constructing such a map poses challenges related to the fusion of multi-modal sensor data, the attainment of real-time mapping performance, and the preservation of structural and semantic information consistency. In this paper, we introduce an online metric-semantic mapping system that utilizes LiDAR-Visual-Inertial sensing to generate a global metric-semantic mesh map of large-scale outdoor environments. Leveraging GPU acceleration, our mapping process achieves exceptional speed, with frame processing taking less than 7ms, regardless of scenario scale. Furthermore, we seamlessly integrate the resultant map into a real-world navigation system, enabling metric-semantic-based terrain assessment and autonomous point-to-point navigation within a campus environment. Through extensive experiments conducted on both publicly available and self-collected datasets comprising 24 sequences, we demonstrate the effectiveness of our mapping and navigation methodologies. Code has been publicly released: https://github.com/gogojjh/cobra