Work

Monocular Depth Estimation Applied to Global Localization Over 2D Floor Plans Using Free Space Density

Year: 2024

Type: article

Abstract: Indoor global localization is a critical aspect of autonomous robotic navigation. The increasing demand for service consumer-grade robots that require self-localization calls for research on methods that work with easy setup and low-cost sensors. In this paper, we propose a monocular camera-based localization of a motorized wheeled robot using a 2D floor plan as a reference map. The innovation of our method lies in using depth maps estimated from monocular images to compute the free space around the robot to be used as a measurement model in a particle filter strategy. The estimated free space density is compared to the free space density extracted from particles in the 2D floor plan. Due to the inherent imperfections of estimated depth maps, we also propose a new particle weighting approach to account for uncertainties in the depth estimation from the monocular camera. Experiments performed using real-world scenario sequences of images comparing the proposed method with RGB-D camera-based approaches demonstrate the effectiveness of the method, even for imperfect depth maps obtained with the monocular depth estimation model.

Source: Journal of Intelligent & Robotic Systems

DOI: 10.1007/s10846-024-02131-1

Authors: Cristian Lopes, Renan Maffei, Mariana Kolberg

Institution: Universidade Federal do Rio Grande do Sul

Language: English

Topic: Robotics and Sensor-Based Localization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,990

Funders: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Universidade Federal do Rio Grande do Sul