A Real-Time Framework for Intermediate Map Construction and Kinematically Feasible Off-Road Planning Without OSM

2025-10-04

AI Summary

To address the limitations of conventional global path planning—namely poor real-time performance, kinematic infeasibility, and high memory overhead—in large-scale off-road environments, this paper proposes an OSM-free, lightweight intermediate mapping framework. It fuses terrain elevation, obstacle geometry, and traversability information into a compact semantic map within a pixel-based coordinate system. Path planning is then performed via a three-stage pipeline: graph-based search, kinematic feasibility verification, and B-spline-based trajectory smoothing. The method supports parallelization of subproblems, significantly improving computational efficiency. Experimental evaluation in unstructured, multi-square-kilometer outdoor scenes demonstrates an average planning latency of 1.5 seconds and peak memory usage of approximately 1.5 GB. The framework has been successfully deployed on search-and-rescue and agricultural robotics platforms, validating its engineering practicality and generalizability across diverse off-road applications.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Off-road environments present unique challenges for autonomous navigation due to their complex and unstructured nature. Traditional global path-planning methods, which typically aim to minimize path length and travel time, perform poorly on large-scale maps and fail to account for critical factors such as real-time performance, kinematic feasibility, and memory efficiency. This paper introduces a novel global path-planning method specifically designed for off-road environments, addressing these essential factors. The method begins by constructing an intermediate map within the pixel coordinate system, incorporating geographical features like off-road trails, waterways, restricted and passable areas, and trees. The planning problem is then divided into three sub-problems: graph-based path planning, kinematic feasibility checking, and path smoothing. This approach effectively meets real-time performance requirements while ensuring kinematic feasibility and efficient memory use. The method was tested in various off-road environments with large-scale maps up to several square kilometers in size, successfully identifying feasible paths in an average of 1.5 seconds and utilizing approximately 1.5GB of memory under extreme conditions. The proposed framework is versatile and applicable to a wide range of off-road autonomous navigation tasks, including search and rescue missions and agricultural operations.

Problem

• Developing real-time off-road navigation without OpenStreetMap data
• Ensuring kinematic feasibility and memory efficiency in path planning
• Constructing intermediate maps with geographical features for autonomous navigation

Innovation

• Constructs intermediate map with geographical features in pixel coordinates
• Divides planning into graph-based path and kinematic feasibility sub-problems
• Ensures real-time performance with efficient memory usage in large-scale maps