6 citations · 8 across the 9 of their papers we have counts for
12 papers
TartanMatch: Towards Universal Dense Correspondence Across Modalities
Hyeokjoon Kwon, Jiting Cai, Ruogu Li +7
Robotic platforms combine multi-modal sensors to operate across diverse conditions, but most dense correspondence methods are limited to RGB-RGB or a small set of modality pairs. T…
AnyThermal: Towards Learning Universal Representations for Thermal Perception
Parv Maheshwari, Jay Karhade, Yogesh Chawla +8
We present AnyThermal, a thermal backbone that captures robust task-agnostic thermal features suitable for a variety of tasks such as cross-modal place recognition, thermal segment…
ThermalDiffusion: Visual-to-Thermal Image-to-Image Translation for Autonomous Navigation
Shruti Bansal, Wenshan Wang, Yifei Liu +1
Autonomous systems rely on sensors to estimate the environment around them. However, cameras, LiDARs, and RADARs have their own limitations. In nighttime or degraded environments s…
Deep Bayesian Future Fusion for Self-Supervised, High-Resolution, Off-Road Mapping
Shubhra Aich, Wenshan Wang, Parv Maheshwari +6
High-speed off-road navigation requires long-range, high-resolution maps to enable robots to safely navigate over different surfaces while avoiding dangerous obstacles. However, du…
TartanDrive 2.0: More Modalities and Better Infrastructure to Further Self-Supervised Learning Research in Off-Road Driving Tasks
Matthew Sivaprakasam, Parv Maheshwari, Mateo Guaman Castro +6
We present TartanDrive 2.0, a large-scale off-road driving dataset for self-supervised learning tasks. In 2021 we released TartanDrive 1.0, which is one of the largest datasets for…
PIAug -- Physics Informed Augmentation for Learning Vehicle Dynamics for Off-Road Navigation
Parv Maheshwari, Wenshan Wang, Samuel Triest +5
Modeling the precise dynamics of off-road vehicles is a complex yet essential task due to the challenging terrain they encounter and the need for optimal performance and safety. Re…
Learning Risk-Aware Costmaps via Inverse Reinforcement Learning for Off-Road Navigation
Samuel Triest, Mateo Guaman Castro, Parv Maheshwari +3
The process of designing costmaps for off-road driving tasks is often a challenging and engineering-intensive task. Recent work in costmap design for off-road driving focuses on tr…
Multiple Waypoint Navigation in Unknown Indoor Environments
Shivam Sood, Jaskaran Singh Sodhi, Parv Maheshwari +2
Indoor motion planning focuses on solving the problem of navigating an agent through a cluttered environment. To date, quite a lot of work has been done in this field, but these me…
[Re] Differentiable Spatial Planning using Transformers
Rohit Ranjan, Himadri Bhakta, Animesh Jha +2
This report covers our reproduction effort of the paper 'Differentiable Spatial Planning using Transformers' by Chaplot et al. . In this paper, the problem of spatial path planning…
Reproducibility Report: Contrastive Learning of Socially-aware Motion Representations
Roopsa Sen, Sidharth Sinha, Parv Maheshwari +2
The following paper is a reproducibility report for "Social NCE: Contrastive Learning of Socially-aware Motion Representations" {\cite{liu2020snce}} published in ICCV 2021 as part…
[Reproducibility Report] Path Planning using Neural A* Search
Shreya Bhatt, Aayush Jain, Parv Maheshwari +2
The following paper is a reproducibility report for "Path Planning using Neural A* Search" published in ICML2 2021 as part of the ML Reproducibility Challenge 2021. The original pa…
Local NMPC on Global Optimised Path for Autonomous Racing
Dvij Kalaria, Parv Maheshwari, Animesh Jha +4
The paper presents a strategy for the control of anautonomous racing car on a pre-mapped track. Using a dynamic model of the vehicle, the optimal racing line is computed, taking tr…