A Сomplex model for predicting the position of a mobile robot moving in an unstructured environment

Abstract

A Сomplex model for predicting the position of a mobile robot moving in an unstructured environment

Pirogov M.A.

Incoming article date: 18.10.2025

An ensemble of models for predicting the position of a mobile robot moving in an unstructured environment is presented. An architecture has been developed that integrates a kinematic motion model with trainable models utilizing elevation map data and semantic segmentation. The principles for constructing a spatial feature map are described, incorporating geometric characteristics such as the terrain roughness index and a fuzzy traversability index. A modular structure of the following blocks is proposed: data preprocessing, geometric property computation, segmentation, and decision-making. Test results demonstrate the advantage of combining kinematic and sensor-based models for autonomous navigation in complex environments.

Keywords: traversability model, elevation map, point cloud, kinematic model, segmentation, machine learning, feature map