Madhu Vankadari

Madhu Vankadari is a doctoral candidate at the University of Oxford's Cyber Physical Systems group, under the supervision of Prof. Niki Trigoni and Prof. Andrew Markhem to Oxford, he worked as a Machine Vision researcher at TCS Research in India. Madhu's research revolves around using deep learning for SLAM-related challenges, such as improving depth estimation, camera pose accuracy, multi-motion scenarios, and visual place recognition. His work finds applications in robotics and computer vision, enhancing areas like autonomous navigation and augmented reality.

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Research Interests

I worked with many robots such as Manipulators, Ground and Aerail Vechicles. I aim to make these robots a little bit safer than today. Currently, I am working on large-scale localization and mapping problems which has potential applications in Autonomous Driving, Augmented and Virtual Reality areas.

Skills: Python | Tensorflow | PyTorch | ROS | Gazebo

Published Venues: ECCV(1) ✔ IJCAI(1) ✔ ICRA(4) ✔ IROS(3) ✔ CORL(1) ✔


  • 15/Sept/2023 - New work on Stereo Depth estimation for night-time, under review
  • 18/Feb/2023 - Paper is accepted at ICRA 2023, London UK
  • 09/Feb/2023 - Finished my Internship at SLAMCore, London, UK
  • 10/Sept/2023 - Paper is accepted at CORL 2023, Aukland, NZ

2024 - 2022 | 2022 - 2020 | 2020 - 2018 | 2018 - before

Dusk Till Dawn: Self-supervised Nighttime Stereo Depth Estimation using Visual Foundation Models
Madhu Vankadari, Samuel Hodgson, Sangyun Shin , Kaichen Zhou , Andrew Markham, Niki Trigoni

W§e introduce an algorithm designed to achieve accurate self-supervised stereo depth estimation specifically for nighttime conditions. Our approach efficiently utilizes features extracted by a pre-trained visual foundation model

Under Review | Abstract |

Sample, Crop, Track: Self-Supervised Mobile 3D Object Detection for Urban Driving LiDAR
Sangyun Shin , Stuart Golodetz, Madhu Vankadari, Andrew Markham, Niki Trigoni

In this paper, we propose a new self-supervised mobile object detection approach called SCT. This uses both motion cues and expected object sizes to improve detection performance, and predicts a dense grid of 3D oriented bounding boxes to improve object discovery

ICRA, 2023 | Abstract | Paper