• MA3 - Model-Accuracy Aware Anytime Planning with Simulation Verification for Navigating Complex Terrains

    Details on our SoCS-2022 paper

  • Online Photometric Calibration of Automatic Gain Thermal Infrared Cameras

    Details on our RAL-2021 paper

  • Plot real-time terminal data

    A python script to plot 1D realtime data being written to the stdout by other application

  • Research and Hobby Videos

    Joint Point Cloud and Image Based Localization For Efficient Inspection in Mixed Reality

    Reference: M. P. Das, Z. Dong, S. Scherer. Joint Point Cloud and Image Based Localization For Efficient Inspection in Mixed Reality. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, 2018.
    PDF: https://arxiv.org/pdf/1811.02563.pdf
    Code: https://bitbucket.org/castacks/jpil

    Augmenting Inspection Capabilities with Mixed-Reality

    We demonstrate the use of mixed-reality headset worn by an inspector on the site of inspection. Such a headset along with the software we have developed, can augment the on-site inspector's capabilities to visualize and interact with digital annotations. Thus allowing precise and clear information about the region of interest.


    Demo Run: Thermal, Monocular Vision-Based Localization and Mapping of MAV at Night.

    Video runs at 0.5x. This video demonstrates how the state-of-the-art monocular computer vision algorithms perform in thermal IR domain. Reference: S. Daftry, M. P. Das, J. Delaune, C. Sorice, R. Hewitt, S. Reddy, D. Lytle, E. Gu, L. Matthies. Robust Vision-based Autonomous Navigation, Mapping and Landing for MAVs at Night. International Symposium on Experimental Robotics (ISER), Buenos Aires, 2018.


    5-DoF Monocular Visual Localization Over Grid Based Floor

    Reference: M. P. Das, G. Gardi, J. Mukhopadhyay. 5-DoF Monocular Visual Localization Over Grid Based Floor. IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sapporo, 2017.
    PDF: https://ieeexplore.ieee.org/document/8115889
    Code: https://github.com/mpdmanash/mlog_ros


    Quadcopter mapping, path planning and controls for Obstacle avoidance.

    Sensors: 2D Lidar, GPS, IMU
    Flight controller: PX4
    Path planning algorithm: RRT# using OMPL C++ (http://ompl.kavrakilab.org/)
    Other libraries used: FCL (A Flexible Collision Library), Octomap, ROS.
    Simulation is made in Gazebo.


    VINS-Mono Visual Inertial Odometry on Optor Stereo Camera

    Camera and IMU calibration performed using kalibr


    Hobby Aeromodelling