most citedBridging the Domain Gap between Synthetic and Real-World Data for Autonomous Driving

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20241 cited

Towards Long Term SLAM on Thermal Imagery

Colin Keil, Aniket Gupta, Pushyami Kaveti +1

Visual SLAM with thermal imagery, and other low contrast visually degraded environments such as underwater, or in areas dominated by snow and ice, remain a difficult problem for ma…

cs.RO2024

On Designing Consistent Covariance Recovery from a Deep Learning Visual Odometry Engine

Jagatpreet Singh Nir, Dennis Giaya, Hanumant Singh

Deep learning techniques have significantly advanced in providing accurate visual odometry solutions by leveraging large datasets. However, generating uncertainty estimates for the…

cs.CV2024

NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots Using Edge Devices

Zhiyong Zhang, Huaizu Jiang, Hanumant Singh

Real-time high-accuracy optical flow estimation is a crucial component in various applications, including localization and mapping in robotics, object tracking, and activity recogn…

cs.RO20231 cited

Bridging the Domain Gap between Synthetic and Real-World Data for Autonomous Driving

Xiangyu Bai, Yedi Luo, Le Jiang +4

Modern autonomous systems require extensive testing to ensure reliability and build trust in ground vehicles. However, testing these systems in the real-world is challenging due to…

cs.CV2023

Bayesian Decision Making to Localize Visual Queries in 2D

Syed Asjad, Aniket Gupta, Hanumant Singh

This report describes our approach for the EGO4D 2023 Visual Query 2D Localization Challenge. Our method aims to reduce the number of False Positives (FP) that occur because of hig…