Fast reliable robot navigation works indoors and outdoors with low hardware needs
Fast and Robust Teach-and-Repeat Navigation Using MixVPR Visual Place Recognition*
Robotics
Summary
Robots need to know where they are to move around safely and reliably. This paper presents a way for robots to remember and recognize places quickly using a method called MixVPR. The system works well even when the environment changes or is messy, both inside buildings and outside. Because it doesn’t need powerful computers, it can be used on many different types of robots. The authors tested their system and found it as accurate as other advanced methods but more efficient.
What this means in practice
- •For robot developers: Build mobile robots that navigate indoors and outdoors with reliable place recognition requiring lower hardware power.
- •For warehouse automation teams: Implement easy-to-deploy navigation for logistics robots that operate in changing warehouse layouts without costly hardware upgrades.
- •For autonomous vehicle engineers: Enhance vehicle localization systems in urban environments with an efficient method that reduces computational demands.$Commercial implications: Enables production of cost-effective navigation hardware for autonomous vehicles by reducing processing requirements.
Authors
Václav Truhlařík, Tomáš Pivoňka, Libor Přeučil
Abstract
Teach-and-repeat navigation systems employing advanced visual place recognition techniques for localization exhibit key attributes for long-term mobile robot navigation, such as the ability to operate in unstructured and dynamic environments. However, existing solutions based on deep-learning techniques are computationally demanding, limiting their applicability. This work introduces a novel and efficient teach-and-repeat system built on the modern visual place recognition method MixVPR. Real-world testing demonstrated its ability to operate both indoors and outdoors, achieving robustness and navigation precision comparable to other state-of-the-art systems. In addition, its lower hardware requirements make it suitable for a wide range of robotic platforms and practical applications.