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The Light Imaging Detection and Ranging (LIDAR) is a method for measuring distances (ranging) by illuminating the target with laser light and measuring the reflection with a sensor. The LIDAR Sensor escalates the entire mechanism with great efficiency which is notified with process and main activation codes.
This package introduces the concept of optimizing target shape to remove pose ambiguity for LiDAR point clouds. Both the simulation and the experimental results confirm that by using the optimal shape and the global solver, we achieve centimeter error in translation and a few degrees in rotation even when a partially illuminated target is placed…
A Jupyter notebook that demonstrates a Python™ implementation of NASA's Airborne Topographic Mapper (ATM) centroid tracker and compares it with results from the equivalent MATLAB® function.
This package introduces the concept of optimizing target shape to remove pose ambiguity for LiDAR point clouds. Both the simulation and the experimental results confirm that by using the optimal shape and the global solver, we achieve centimeter error in translation and a few degrees in rotation even when a partially illuminated target is placed…
[RA-L 2020] Official Tensorflow Implementation for "RGGNet: Tolerance Aware LiDAR-Camera Online Calibration with Geometric Deep Learning and Generative Model", IEEE Robotics and Automation Letters 5.4 (2020): 6956-6963
This is a fiducial marker system designed for LiDAR sensors. Different visual fiducial marker systems (Apriltag, ArUco, CCTag, etc.) can be easily embedded. The usage is as convenient as that of the visual fiducial marker. The system shows potential in SLAM, multi-sensor calibration, augmented reality, and so on.