PathSpace: Rapid continuous map approximation for efficient SLAM using B-Splines in constrained environments

2026-03-02

AI Summary

This work addresses the challenge of balancing semantic constraints and computational efficiency in traditional semantic SLAM, which often relies on dense geometric representations and struggles in sparse or highly structured environments. To overcome this limitation, the authors propose PathSpace, a novel framework that introduces B-splines into semantic SLAM for the first time. By leveraging compact, continuous environmental modeling coupled with joint inference via probability density functions, PathSpace enables efficient sparse representation guided by structural priors. Evaluated in autonomous racing scenarios, the method significantly reduces map complexity and computational resource consumption while maintaining localization accuracy comparable to conventional landmark-based approaches, thereby demonstrating its efficacy and practicality.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Simultaneous Localization and Mapping (SLAM) plays a crucial role in enabling autonomous vehicles to navigate previously unknown environments. Semantic SLAM mostly extends visual SLAM, leveraging the higher density information available to reason about the environment in a more human-like manner. This allows for better decision making by exploiting prior structural knowledge of the environment, usually in the form of labels. Current semantic SLAM techniques still mostly rely on a dense geometric representation of the environment, limiting their ability to apply constraints based on context. We propose PathSpace, a novel semantic SLAM framework that uses continuous B-splines to represent the environment in a compact manner, while also maintaining and reasoning through the continuous probability density functions required for probabilistic reasoning. This system applies the multiple strengths of B-splines in the context of SLAM to interpolate and fit otherwise discrete sparse environments. We test this framework in the context of autonomous racing, where we exploit pre-specified track characteristics to produce significantly reduced representations at comparable levels of accuracy to traditional landmark based methods and demonstrate its potential in limiting the resources used by a system with minimal accuracy loss.

Problem

• Semantic SLAM
• dense geometric representation
• contextual constraints
• efficient mapping
• autonomous racing

Innovation

• B-Splines
• Semantic SLAM
• continuous map representation
• constrained environments
• compact mapping