Comparison of Three Off-the-Shelf Visual Odometry Systems

Alexandre Alapetite,Mikołaj Patalan,Zhongyu Wang,John Paulin Hansen,Marcin Zajączkowski

doi:10.3390/robotics9030056

Alexandre Alapetite, Mikołaj Patalan + Show 3 more

Open Access

https://doi.org/10.3390/robotics9030056

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Abstract

Positioning is an essential aspect of robot navigation, and visual odometry an important technique for continuous updating the internal information about robot position, especially indoors without GPS (Global Positioning System). Visual odometry is using one or more cameras to find visual clues and estimate robot movements in 3D relatively. Recent progress has been made, especially with fully integrated systems such as the RealSense T265 from Intel, which is the focus of this article. We compare between each other three visual odometry systems (and one wheel odometry, as a known baseline), on a ground robot. We do so in eight scenarios, varying the speed, the number of visual features, and with or without humans walking in the field of view. We continuously measure the position error in translation and rotation thanks to a ground truth positioning system. Our result shows that all odometry systems are challenged, but in different ways. The RealSense T265 and the ZED Mini have comparable performance, better than our baseline ORB-SLAM2 (mono-lens without inertial measurement unit (IMU)) but not excellent. In conclusion, a single odometry system might still not be sufficient, so using multiple instances and sensor fusion approaches are necessary while waiting for additional research and further improved products.

Highlights

Robot localization within its environment is one of the fundamental problems in the field of mobile robotics [1]
An important feature to improve the quality of vision-based odometry (VO) is to use SLAM [10] supplemented by “loop closure”: this means building a database of images while moving, so that when the robot comes back to an already seen location—i.e., with a view similar enough than one of those in the database—it will relocalize itself, thereby cancelling any drift since the last time the robot was at that same location, which results in a much more robust long-time positioning [11]
The RealSense T265 globally wins over the ZED Mini and ORB-SLAM2, as it comes with built-in data processing, while the other visual odometries require an additional powerful computer board such as an NVIDIA Jetson or similar

Summary

Introduction

Robot localization within its environment is one of the fundamental problems in the field of mobile robotics [1]. One way of tracking this problem is to use vision-based odometry (VO), that is capable of accurately localizing robots’ position with low drift over long trajectories even in challenging conditions. Many VO algorithms were developed that are categorized into direct, semi-direct and feature-based on what image information is used in order to estimate egomotion [2]. An important feature to improve the quality of VO is to use SLAM (simultaneous localization and mapping) [10] supplemented by “loop closure”: this means building a database of images while moving, so that when the robot comes back to an already seen location—i.e., with a view similar enough than one of those in the database—it will relocalize itself, thereby cancelling any drift since the last time the robot was at that same location, which results in a much more robust long-time positioning [11]

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