|
Email: bduister@cmu.edu I am a Ph.D. student at Carnegie Mellon University (CMU), in the Robotics Institute advised by Sebastian Scherer. Before coming to CMU, I completed a Bachelor's and Master's degree in Aerospace Engineering at Delft University of Technology, in the Netherlands. Advised by Guido de Croon, I studied efficient bio-inspired algorithms for autonomous flight of tiny flying drones. In 2019 I was a visiting student at Vijay Janapa Reddi's Edge Computing lab, at Harvard University, where we studied Deep Reinforcement Learning for tiny robots. During this time my work on bio-inspired intelligence with Guido de Croon also helped me receive the titel of best graduate in engineering of TU Delft in the academic year 2020-2021. My research focus continues to be on scalable AI for fully autonomous operation of robots that interact with their environment. At the intersection of computer vision, machine learning, and systems, I hope to contribute to a future where complex robotic automation is scalable, safe and useful.CV / Google Scholar / Twitter / LinkedIn / Github |
|
|
|
|
Bardienus P Duisterhof, Yaoyu Hu, Si Heng Teng, Michael Kaess, Sebastian Scherer Under Review - ICRA 2023 project website / arXiv / video / code In this work we present our methodology for accurate wide-angle calibration. Our pipeline generates an intermediate model, and leverages it to iteratively improve feature detection and eventually the camera parameters. |
|
Bardienus P Duisterhof, Shushuai Li, Javier Burgués, Vijay Janapa Reddi, Guido C.H.E. de Croon IROS, 2021 arXiv / video / code We have developed a swarm of autonomous, tiny drones that is able to localize gas sources in unknown, cluttered environments. Bio-inspired AI allows the drones to tackle this complex task without any external infrastructure. |
|
Bardienus P Duisterhof, Srivatsan Krishnan, Jonathan J. Cruz, Colby R. Banbury, William Fu, Aleksandra Faust , Guido C.H.E. de Croon, Vijay Janapa Reddi ICRA, 2021 paper / video / code We present fully autonomous source seeking onboard a highly constrained nano quadcopter, by contributing application-specific system and observation feature design to enable inference of a deep-RL policy onboard a nano quadcopter. |
|
Diana A. Olejnik, Bardienus P Duisterhof, Matej Karásek , Kirk Y. W. Scheper, Tom van Dijk, Guido C.H.E. de Croon Unmanned Systems, Vol. 08, No. 04, pp. 287-294 , 2020 paper / video This paper describes the computer vision and control algorithms used to achieve autonomous flight with the ∼30g tailless flapping wing robot, used to participate in the International Micro Air Vehicle Conference and Competition (IMAV 2018) indoor microair vehicle competition. |
|
|
| AE2235-I: Aerospace Systems & Control Theory |
|
|
|
Forbes IEEE Spectrum Video Friday Robohub Bitcraze Blog PiXL Drone Show |
|
|
|
Best Graduate in Engineering, TU Delft, academic year 2020-2021 Best Graduate in Aerospace Engineering, TU Delft, academic year 2020-2021 Innovation Award, IMAV 2018 Autonomous Drone Race |
|
Modified version of template from here |