Udit Singh Parihar

Udit Singh Parihar

Deep Learning and SLAM Researcher

OLA Electric


Hey! I am currently at OLA Electric working on 3D scene representation of end-to-end autonomous driving agents and Mapping and Localization. Our work on deep feature matching has achieved a silver medal in the Kaggle Image Matching Challenge. I have been working on porting PyTorch model to the TensorRT for faster inference on resource-constraint devices and developing ROS2 wrapper around the deep learning pipeline.

Previously, I have done my master’s from IIITH from Robotics Research Center where I have been working on the intersection of SLAM, 3D computer vision, and deep learning with Prof Madhava Krishna. Our work on robust pose graph SLAM has been published at the ICRA conference and our work on Robust feature matching has been presented at the IROS conference. These works targets improving SLAM performance in feature-less regions and improving correspondence matching in high illumination and viewpoint variations.

Download my resumé.


  • SLAM / State Estimation
  • Differentiable Computer Vision
  • Optimization
  • Robotics


  • MS by Research in CSE with specialization in Robotics, 2021

    International Institute of Information Technology, Hyderabad

  • B.Tech in Mechanical, 2018

    Indian Institute of Technology Jodhpur



Computer Vision and SLAM Research Engineer

OLA Electric

Jul 2021 – Jan 2023 Bangalore
  • Developed an end to end autonomous driving agent using cameras, GPS and IMU sensors. Ported the agent from Carla simulator to NuScenes Dataset.
  • Converted the pytorch model to TensorRT and developed a ROS wrapper to run on real Mahindra E2O car achieving final control prediction at 25 HZ, in a zero shot paradigm.
  • Won the silver medal in the Kaggle Image Matching Challenge 2022 by developing an Ensemble of Deep feature matching algorithm of SuperGlue and LoFTR.
  • Extended the Lidar based mapping and localization LeGO-LOAM SLAM for the Velodyne and Ouster lidars and ported ROS1 to ROS2 in C++.
  • Trained Self Supervised Depth estimation PackNet-SfM on Indian driving dataset and on Carla simulator dataset.

Graduate Research Assistant

Robotics Research Center, IIITH

Aug 2018 – Jul 2021 Hyderabad
  • Worked on the intersection of SLAM, Computer Vision, Deep Learning, and Robotics. Developed robust pose graph constraints using scene semantics and developed rotation invariant deep feature descriptors for feature matching.
  • Published in ICRA and IROS conferences.

Publications & Preprint

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Parallel Computing Toolbox

Implementation of PCA algorithms for image compression using C++/Cuda and parallel Monte Carlo algorithm using OpenMP and MPI from scratch

Robotics Toolbox

Implementation of common robotics algorithms like Bundle Adjustment, Visual Odometry, Stereo Reconstruction and EKF from scratch

Tutorial on pose graph optimization using g2o

Example of Pose Graph SLAM and landmark based SLAM using syntheic dataset