In this presentation, we describe and demonstrate a novel vision-enhanced low-light imager system to provide GPS-denied navigation and ML-based visual scene understanding capabilities for both day and night operations.
Computer vision publications
Vision based Navigation using Cross-View Geo-registration for Outdoor Augmented Reality and Navigation Applications
In this work, we present a new vision-based cross-view geo-localization solution matching camera images to a 2D satellite/ overhead reference image database. We present solutions for both coarse search for cold start and fine alignment for continuous refinement.
Cross-View Visual Geo-Localization for Outdoor Augmented Reality
We address the problem of geo-pose estimation by cross-view matching of query ground images to a geo-referenced aerial satellite image database. Recently, neural network-based methods have shown state-of-the-art performance in cross-view matching.
On auxiliary latitudes
The auxiliary latitudes are essential tools in cartography. This paper summarizes methods for converting between them with an emphasis on providing full double-precision accuracy.
Autonomous Docking Using Learning-Based Scene Segmentation in Underground Mine Environments
This paper describes a vision-based autonomous docking solution that moves a coalmine shuttle car to the continuous miner in GPS-denied underground environments.
Sensor Control for Information Gain in Dynamic, Sparse and Partially Observed Environments
We present an approach for autonomous sensor control for information gathering under partially observable, dynamic and sparsely sampled environments.
Ranging-Aided Ground Robot Navigation Using UWB Nodes at Unknown Locations
This paper describes a new ranging-aided navigation approach that does not require the locations of ranging radios.
Low-Power In-Pixel Computing with Current-Modulated Switched Capacitors
We present a scalable in-pixel processing architecture that can reduce the data throughput by 10X and consume less than 30 mW per megapixel at the imager frontend.
Unpacking Large Language Models with Conceptual Consistency
We propose conceptual consistency to measure a LLM’s understanding of relevant concepts. This novel metric measures how well a model can be characterized by finding out how consistent its responses to queries about conceptually relevant background knowledge are.