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Abhinav Valada

Albert-Ludwigs-Universität Freiburg
Technische Fakultät
Autonome Intelligente Systeme
Georges-Köhler-Allee 080
D-79110 Freiburg i. Br., Germany
Office:   080-022

Phone:   +49 761 203-8025
Fax:   +49 761 203-8007

About me

  • 09/2014 - up to now: Working at the Autonomous Intelligent Systems group of Prof. Dr. Wolfram Burgard as Ph.D. student
  • 07/2013 - 07/2014: Systems Engineer, National Robotics Engineering Center, USA
  • 01/2012 - 12/2013: M.S. in Robotics, Carnegie Mellon University, USA
  • 11/2011 - 06/2013: Systems/Software Engineer, Field Robotics Center, Carnegie Mellon University, USA
  • 01/2010 - 10/1011: Staff Research Assistant, Field Robotics Center, Carnegie Mellon University, USA
  • 06/2006 - 08/2010: B.Tech. in Electronics and Instrumentation Engineering, VIT University, India

  • My Erdös numberis at most 4.

    Publications and ongoing research can be found on the left panel.

    Download my full resume here

Research Interests

  • Deep Convolutional Neural Networks
    • Scene Understanding - Multiclass recognition/detection, segmentation
    • Mixture of Deep Experts - Multimodal fusion, self-supervised learning
    • Uncertainty Quantification - Confidence estimation, active learning
  • Deep Multitask Learning
  • Visual Learning

Research Projects

  • FOUNT^2-Fliegendes Lokalisierungssystem für die Rettung und Bergung von Verschütteten.
  • RLDL-Robust Localization using Deep Landmark Features.
  • LifeNav-Reliable lifelong navigation for mobile robots.
  • ZAFH-AAL-Zentrum für Angewandte Forschung an Hochschulen für Ambient Assisted Living (Collaborative Center for Applied Research on Ambient Assisted Living)


Students Supervised

  • Landmark-based Visual Localization using Deep Convolutional Neural Networks, Jay Patravali (together with Noha Radwan), 2017, Internship (Ongoing)
  • Room Layout Estimation using Deep Convolutional Neural Networks, Louay Abdelgawad, 2017, Master's Project (Ongoing)
  • Multimodal Localization using Deep Convolutional Neural Networks, Hanna Stellmach (together with Noha Radwan), 2017, Master's Project
  • Predicting Landing Sites in Aerial Images from Disaster Scenarios, Mayank Mittal, 2017, DAAD Internship
  • Laser-Camera Label Transfer for Semantic Segmentation, Rohit Suri, 2017, DAAD Internship
  • Semantic Segmentation of Moving Objects, Johan Vertens, 2016, Master's Thesis
  • Robust Deep Semantic Segmentation using Convoluted Mixture of Deep Experts, Ankit Dhall, 2016, DAAD Internship
  • Multimodal Vegetation Segmentation using Up-Convolutional Neural Networks, Julian Kunzelmann, 2016, Bachelor's Thesis
  • Navigational Autonomy for Nano-Quadrotors, Gonzalo Nuno Estevez, 2015, Bachelor's Thesis


  • Sep 2017: "Perspectives on Deep Multimodel Robot Learning" is accepted as one of the 16 papers selected for ISRR'17 Blue Sky Papers
  • July 2017: "Robust Proprioceptive Terrain Classification" is accepted for IJRR Special Issue on Robotics Research
  • June 2017: "SMSnet: Semantic Motion Segmentation" is accepted for IROS'17
  • Jan 2017: "AdapNet: Adaptive Semantic Segmentation" is accepted for ICRA'17
  • Oct 2016: Benjamin and I will give an invited talk on "Techniques for Reliable Robot Perception in Unstructured Environments" at IROS'16 workshop
  • Sep 2016: Adaptive Semantic Segmentation to be presented at NVIDIA GTC EUROPE 2016
  • Aug 2016: Paper accepted for IROS'16 workshop on State Estimation and Terrain Perception for All Terrain Mobile Robots
  • June 2016: Deep MultiSpectral Scene Understanding accepted for oral presentation at ISER'16
  • May 2016: Paper accepted for Deutsche Gesellschaft für Robotik-Tage 2016 (DGR Days 2016)
  • May 2016: Paper accepted for RSS'16 workshop on the Limits and Potentials of Deep Learning in Robotics
  • Mar 2016: Acoustics-based terrain classification selected for IJRR special issue on Robotics Research
  • Jan 2016: Two papers accepted for ICRA'16
  • Sept 2015: Checkout our DeepTerrain Social experiment
  • Jul 2015: Acoustics based Terrain Classification accepted for oral presentation at ISRR'15
  • Jun 2015: Paper accepted for RSS'15 workshop on Model Learning for Human-Robot Communication




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