Pony.ai. has been granted a patent for a method that enhances vehicle sensor calibration by detecting static objects in the environment. The process involves capturing point cloud data and iteratively adjusting the first sensor’s transformation matrix to achieve accurate calibration to a global coordinate system using a pre-calibrated second sensor. GlobalData’s report on Pony.ai gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Pony.ai, Autonomous freight management was a key innovation area identified from patents. Pony.ai's grant share as of June 2024 was 56%. Grant share is based on the ratio of number of grants to total number of patents.
Vehicle sensor calibration using detected static objects
The patent US12032102B2 outlines a computer-implemented method and system for calibrating a first sensor, such as a LiDAR sensor, associated with a vehicle by utilizing detected static objects in the vehicle's environment. The method begins with the detection of static objects and the assessment of their height and shape variances. If these variances exceed a predetermined threshold, point cloud data is captured by the first sensor, which is then calibrated to a global coordinate system using a pre-calibrated second sensor. The calibration process involves determining a transformation matrix that aligns the local coordinate systems of both sensors, ensuring that the point cloud data accurately reflects the position of the detected static objects as the vehicle moves.
Further claims detail the calibration process, including the determination of rotational and translational components of the transformation matrix, and the evaluation of calibration accuracy against a set threshold. If the calibration accuracy is found lacking, the method allows for the detection of additional static objects and the recalibration of the first sensor based on new point cloud data. The system also incorporates a mechanism to assess environmental conditions prior to calibration, ensuring optimal conditions for accurate sensor calibration. This comprehensive approach aims to enhance the reliability and precision of sensor data in vehicle navigation and environmental interaction.
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