We propose C-SFDA, a curriculum learning aided self-training framework for SFDA that adapts efficiently and reliably to changes across domains based on selective pseudo-labeling. Specifically, we employ a curriculum learning scheme to promote learning from a restricted amount of pseudo labels selected based on their reliabilities.
Night-Time GPS-Denied Navigation and Situational Understanding Using Vision-Enhanced Low-Light Imager
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.
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.
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.
This paper describes a new ranging-aided navigation approach that does not require the locations of ranging radios.
Optimized Simultaneous Aided Target Detection and Imagery based Navigation in GPS-Denied Environments
We describe and demonstrate a comprehensive optimized vision-based real-time solution to provide SATIN capabilities for current and future UAS in GPS-denied environments.
Cross-View and Cross-Modal Visual Geo-Localization for Augmented Reality and Robot/ Vehicle Navigation Applications
We will present methods and results for estimation of geo-location and/ or orientation for dismounts and platforms for wide area, outdoor augmented reality and other applications under GPS denied/ challenged conditions.
We propose a method to train an autonomous agent to learn to accumulate a 3D scene graph representation of its environment by simultaneously learning to navigate through said environment.
SASRA: Semantically-aware Spatio-temporal Reasoning Agent for Vision-and-Language Navigation in Continuous Environments
This paper presents a novel approach for the Vision-and-Language Navigation (VLN) task in continuous 3D environments.