Visual-Geometry GP-based Navigable Space for Autonomous Navigation

TL;DR

This paper introduces VG-SGP, a novel Gaussian process-based framework that integrates semantic and geometric information for improved autonomous navigation in unknown environments.

Contribution

The paper presents a new space modeling framework combining dual Sparse Gaussian Processes to jointly predict semantic and geometric navigable spaces.

Findings

VG-SGP outperforms purely visual or geometric models in experiments.

The integrated model accurately identifies overlapping navigable areas.

Simulation and real-world tests confirm improved navigation performance.

Abstract

Autonomous navigation in unknown environments is challenging and demands the consideration of both geometric and semantic information in order to parse the navigability of the environment. In this work, we propose a novel space modeling framework, Visual-Geometry Sparse Gaussian Process (VG-SGP), that simultaneously considers semantics and geometry of the scene. Our proposed approach can overcome the limitation of visual planners that fail to recognize geometry associated with the semantic and the geometric planners that completely overlook the semantic information which is very critical in real-world navigation. The proposed method leverages dual Sparse Gaussian Processes in an integrated manner; the first is trained to forecast geometrically navigable spaces while the second predicts the semantically navigable areas. This integrated model is able to pinpoint the overlapping (geometric and semantic) navigable space. The simulation and real-world experiments demonstrate that the ability of the proposed VG-SGP model, coupled with our innovative navigation strategy, outperforms models solely reliant on visual or geometric navigation algorithms, highlighting a superior adaptive behavior.