Work

Fusing Direct and Indirect Visual Odometry for SLAM: An ICM-Based Framework

Year: 2025

Type: article

Abstract: The loss of localization in robots navigating GNSS-denied environments poses a critical challenge that can compromise mission success and safe operation. This article presents a method that fuses visual odometry outputs from both direct and feature-based (indirect) methods using Iterated Conditional Modes (ICMs), an efficient iterative optimization algorithm that maximizes the posterior probability in Markov random fields, combined with uncertainty-aware gain adjustment to perform pose estimation and mapping. The proposed method enhances the performance of visual localization and mapping algorithms in low-texture or visually degraded scenarios. The method was validated using the TUM RGB-D benchmark dataset and through real-world tests in both indoor and outdoor environments. Outdoor experiments were conducted on an electric vehicle, where the method maintained stable tracking. These initial results suggest that the technique could be transferable to electric vehicle platforms and applicable in a variety of real-world conditions.

Source: World Electric Vehicle Journal

DOI: 10.3390/wevj16090510

Authors: Jeremias Gaia, Javier Gimenez, Eugenio Orosco, Francisco Rossomando, Carlos Soria, Fernando Ulloa-Vásquez

Institutions: Consejo Nacional de Investigaciones Científicas y Técnicas, National University of San Juan, Metropolitan University of Technology

Language: English

Topic: Robotics and Sensor-Based Localization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,689