Sai Shankar Narasimhan

I am a second year graduate student in the University of Texas at Austin, pursuing MS in ECE. I work with Prof. Sandeep Chinchali in the intersection of Robotics and Computer Vision. Previously, I was a Research Assistant at Robotics Research Centre, IIIT, Hyderabad, working under Madhava Krishna.

I received my undergraduate degree in Electrical Engineering at Anna University in India, with my Bachelor's thesis advised by Ranganath Muthu. My Research Internship at IIT Madras, with P.V.Manivannan, was focussed on SLAM. I have also worked full-time for over a year at Swaayatt Robots.

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• June 2020: AutoLay, our benchmark for amodal layout estimation was accepted for IROS 2020
• Jan 2020: Our work, Deep Flow guided IBVS will be presented in ICRA 2020
• Dec 2019: Our work MonoLayout was accepted for WACV 2020
• June 2019: Joined Robotics Research Centre at IIIT-Hyderabad as Research Assistant

We introduce AutoLay, a new dataset for amodal layout estimation in bird’s eye view. Further, we propose VideoLayout, a real-time neural net architecture that leverages temporal information from monocular video, to produce more accurate and consistent layouts.

We present MonoLayout, a deep neural network for real-time amodal scene layout estimation from a single image. We leverage adversarial feature learning to hallucinate plausible completions for occlusions.

We propose a novel system consisting of deep neural networks that systematically integrates depth cues with flow features.

We propose a novel Kalman Filter (KF) algorithm, that leverages the statistical correlation between the measured variables.

We propose a solution to reduce the range and bearing error in SONARs significantly. Using the results from the Gaussian Correlation Inequality, we derive probabilistic transformations that can improve the measurements of the SONAR, thus reducing the sensor error.

We developed a collaborative Aerial Vehicle (UAV), Ground Vehicle (UGV) platform that can be used to aid / automate search and rescue missions in disaster zones.