Lightweight Visual Odometry for Autonomous Mobile Robots

Sensors
Mohamed Aladem, Samir A Rawashdeh

Abstract

Vision-based motion estimation is an effective means for mobile robot localization and is often used in conjunction with other sensors for navigation and path planning. This paper presents a low-overhead real-time ego-motion estimation (visual odometry) system based on either a stereo or RGB-D sensor. The algorithm's accuracy outperforms typical frame-to-frame approaches by maintaining a limited local map, while requiring significantly less memory and computational power in contrast to using global maps common in full visual SLAM methods. The algorithm is evaluated on common publicly available datasets that span different use-cases and performance is compared to other comparable open-source systems in terms of accuracy, frame rate and memory requirements. This paper accompanies the release of the source code as a modular software package for the robotics community compatible with the Robot Operating System (ROS).

Citations

Feb 1, 2019·Sensors·Hernan GonzalezAbdelhafid Elouardi
Feb 12, 2020·Sensors·Cosimo PatrunoEttore Stella
Feb 13, 2020·Sensors·Oscar Reinoso, Luis Payá
Dec 2, 2020·Sensors·Jorge De LeónAntonio Barrientos
Jan 2, 2021·The Science of the Total Environment·Tyrell Lewis, Kiran Bhaganagar

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Methods Mentioned

BETA
feature extraction

Software Mentioned

LVT
general optimization (
LIBVISO2
Raspberry
AGAST
ODROID
Robot Operating System ( ROS )
System Monitor
g2o )
Ubuntu

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