72 citations · 84 across the 5 of their papers we have counts for
5 papers
RLM: A Vision-Language Model Approach for Radar Scene Understanding
Pushkal Mishra, Kshitiz Bansal, Dinesh Bharadia
Radar sensors provide reliable perception across adverse weather, lighting, and long-range conditions, yet existing machine learning approaches remain fragmented and task-specific,…
CalTag: Robust calibration of mmWave Radar and LiDAR using backscatter tags
Junyi Xu, Kshitiz Bansal, Dinesh Bharadia
The rise of automation in robotics necessitates the use of high-quality perception systems, often through the use of multiple sensors. A crucial aspect of a successfully deployed m…
R-fiducial: Reliable and Scalable Radar Fiducials for Smart mmwave Sensing
Kshitiz Bansal, Manideep Dunna, Sanjeev Anthia Ganesh +2
Millimeter wave sensing has recently attracted a lot of attention given its environmental robust nature. In situations where visual sensors like cameras fail to perform, mmwave rad…
RadSegNet: A Reliable Approach to Radar Camera Fusion
Kshitiz Bansal, Keshav Rungta, Dinesh Bharadia
Perception systems for autonomous driving have seen significant advancements in their performance over last few years. However, these systems struggle to show robustness in extreme…
Pointillism: Accurate 3D bounding box estimation with multi-radars
Kshitiz Bansal, Keshav Rungta, Siyuan Zhu +1
Autonomous perception requires high-quality environment sensing in the form of 3D bounding boxes of dynamic objects. The primary sensors used in automotive systems are light-based…