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I am really impressed by how easy Spectacular AI makes visual-inertial odometry with an OAK camera. Just "pip install spectacularAI" and run a Python script. Running VIO/SLAM used to be much harder in my opinion.

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Yu Xiang

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70,481 görüntüleme • 6 ay önce •via X (Twitter)

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Easiest way to calibrate a camera on Linux. No ROS, no Docker, no CMake. Just FFmpeg and Spectacular AI command line tool installable via Python pip: sudo apt install ffmpeg pip install spectacularAI[full] sai-cli calibrate your-data.mkv
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Easiest way to calibrate a camera on Linux. No ROS, no Docker, no CMake. Just FFmpeg and Spectacular AI command line tool installable via Python pip: sudo apt install ffmpeg pip install spectacularAI[full] sai-cli calibrate your-data.mkv

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Can an inexpensive, off-the-shelf IMU be the only sensor to estimate the full state (position, velocity, orientation) of a quadrotor flying through a track at high speed and even be on-pair with vision-based localization? The answer is yes, within certain limitations! In this #RAL2023 paper, we propose a learning-based odometry algorithm that couples a model-based filter driven by the inertial measurements with a learning-based module with access to the control commands. Our system outperforms by a large margin the state-of-the-art visual-inertial odometry (#VIO) algorithms and the state-of-the-art learned-inertial odometry algorithm, #TLIO, for the task of drone racing. Additionally, we show that our system is as accurate as a VIO algorithm that uses a camera to localize to a known map of the racing track. The main limitation of our approach is that it cannot generalize to trajectories that have not been seen at training time. However, in drone racing competitions, the track is known beforehand. Human pilots spend hours or even days of practice on the race track before the competition. Similarly, our system can be trained with the data collected during practice time and deployed during the competition. Future work will investigate how to generalize to trajectories not seen at training time. The code is released! Paper: Video: Code: Kudos to Giovanni Cioffi Leonard Bauersfeld Elia Kaufmann European Research Council (ERC) University of Zurich UZH Science UZH Space Hub NCCR Robotics Aerial Core #RAL2023 #IROS2023 #SLAM

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