2 citations · 2 across the 4 of their papers we have counts for
5 papers
SparseLoc: Sparse Open-Set Landmark-based Global Localization for Autonomous Navigation
Pranjal Paul, Vineeth Bhat, Tejas Salian +4
Global localization is a critical problem in autonomous navigation, enabling precise positioning without reliance on GPS. Modern global localization techniques often depend on dens…
Imagine-2-Drive: Leveraging High-Fidelity World Models via Multi-Modal Diffusion Policies
Anant Garg, K Madhava Krishna
World Model-based Reinforcement Learning (WMRL) enables sample efficient policy learning by reducing the need for online interactions which can potentially be costly and unsafe, es…
Towards Global Localization using Multi-Modal Object-Instance Re-Identification
Aneesh Chavan, Vaibhav Agrawal, Vineeth Bhat +4
Re-identification (ReID) is a critical challenge in computer vision, predominantly studied in the context of pedestrians and vehicles. However, robust object-instance ReID, which h…
LeGo-Drive: Language-enhanced Goal-oriented Closed-Loop End-to-End Autonomous Driving
Pranjal Paul, Anant Garg, Tushar Choudhary +2
Existing Vision-Language models (VLMs) estimate either long-term trajectory waypoints or a set of control actions as a reactive solution for closed-loop planning based on their ric…
Talk2BEV: Language-enhanced Bird's-eye View Maps for Autonomous Driving
Tushar Choudhary, Vikrant Dewangan, Shivam Chandhok +6
Talk2BEV is a large vision-language model (LVLM) interface for bird's-eye view (BEV) maps in autonomous driving contexts. While existing perception systems for autonomous driving s…