Shreyam Gupta | Searcharxiv

arXiv2026

Drone-Assisted UAV-UGV Collaboration for Autonomous Navigation in Snow-Covered Terrain

This paper presents a collaborative UAV-UGV navigation framework for high-altitude, snow-covered terrain, where reduced visibility and unstable ground render conventional methods ineffective. We introduce a custom efficient U-Net architecture that falls under the computational constraints for real-time road segmentation, utilizing a novel synthetic snow data augmentation technique to achieve 96.5% segmentation accuracy. For UAV localization, we implement an Extended Kalman Filter (EKF) fusing onboard GPS and IMU data, achieving a maximum observed positional error of +-0.5 meters. The UGV position is determined via a visual tracking pipeline using YOLOv5 and depth data from the UAV's RGB-D camera. A dynamic path planning algorithm utilizes this segmentation to adjust for snow drifts, enabling successful navigation in obscured test environment with minimal deviation.

cs.RO↗

arXiv2025

Resolving Spatio-Temporal Entanglement in Video Prediction via Multi-Modal Attention

The fast progress in computer vision has necessitated more advanced methods for temporal sequence modeling. This area is essential for the operation of autonomous systems, real-time surveillance, and predicting anomalies. As the demand for accurate video prediction increases, the limitations of traditional deterministic models, particularly their struggle to maintain long-term temporal coherence while providing high-frequency spatial detail, have become very clear. This report provides an exhaustive analysis of the Multi-Attention Unit Cell (MAUCell), a novel architectural framework that represents a significant leap forward in video frame prediction. By synergizing Generative Adversarial Networks (GANs) with a hierarchical "STAR-GAN" processing strategy and a triad of specialized attention mechanisms (Temporal, Spatial, and Pixel-wise), the MAUCell addresses the persistent "deep-in-time" dilemma that plagues Recurrent Neural Networks (RNNs). Our analysis shows that the MAUCell framework successfully establishes a new state-of-the-art benchmark, especially in its ability to produce realistic video sequences that closely resemble real-world footage while ensuring efficient inference for real-time deployment. Through rigorous evaluation on datasets: Moving MNIST, KTH Action, and CASIA-B, the framework shows superior performance metrics, especially in Learned Perceptual Image Patch Similarity (LPIPS) and Structural Similarity Index (SSIM). This success confirms its dual-pathway information transformation system. This report details the theoretical foundations, detailed structure and broader significance of MAUCell, presenting it as a valuable solution for video forecasting tasks that require high precision and limited resources.

cs.CV↗