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Visual navigation is a key enabling technology for autonomous mobile vehicles. The ability to provide large‐scale, long‐term navigation using low‐cost, low‐power vision sensors is appealing for industrial applications. A crucial requirement for long‐term navigation systems is the ability to localize in environments whose appearance is constantly changing over time—due to lighting, weather, seasons,...
Our work builds upon Visual Teach & Repeat 2 (VT&R2): a vision-in-the-loop autonomous navigation system that enables the rapid construction of route networks, safely built through operator-controlled driving. Added routes can be followed autonomously using visual localization. To enable long-term operation that is robust to appearance change, its Multi-Experience Localization (MEL) leverages...
Vision‐based, autonomous, route‐following algorithms enable robots to autonomously repeat manually driven routes over long distances. Through the use of inexpensive, commercial vision sensors, these algorithms have the potential to enable robotic applications across multiple industries. However, in order to extend these algorithms to long‐term autonomy, they must be able to operate over long periods...
Vision-based, route-following algorithms enable autonomous robots to repeat manually taught paths over long distances using inexpensive vision sensors. However, these methods struggle with long-term, outdoor operation due to the challenges of environmental appearance change caused by lighting, weather, and seasons. While techniques exist to address appearance change by using multiple experiences over...
Mobile robots supported by an electromechanical tether can safely explore extremely rugged terrain in resource-limited environments. While a tether provides power, wired communication, and support on steep surfaces, it also reduces maneuverability; in cluttered environments the tether will contact obstacles, forming intermediate anchor points. In order for the robot to avoid tether entanglement, it...
Camera-based localization techniques must be robust to correspondence errors, i.e., when visual features (landmarks)are matched incorrectly. The two primary techniques to address this issue are RANSAC and robust M-estimation -- each more appropriate for different applications. This paper investigates the use of different robust cost functions for M-estimation to deal with correspondence outliers,...
Stereo Visual Teach & Repeat (VT&R) is a system for long-range, autonomous route following in unstructured 3D environments. As this system relies on a passive sensor to localize, it is highly susceptible to changes in lighting conditions. Recent work in the optics community has provided a method to transform images collected from a three-channel passive sensor into color-constant images that...
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