View source for 17739530. SENSOR CALIBRATION VALIDATION simplified abstract (Zoox, Inc.)

=SENSOR CALIBRATION VALIDATION=


==Organization Name==

[[:Category:Zoox, Inc.|Zoox, Inc.]]


[[Category:Zoox, Inc.]]

==Inventor(s)==

[[:Category:Derek Adams of Pasadena CA (US)|Derek Adams of Pasadena CA (US)]][[Category:Derek Adams of Pasadena CA (US)]]

[[:Category:Zakieh Sadat Hashemifar of Sunnyvale CA (US)|Zakieh Sadat Hashemifar of Sunnyvale CA (US)]][[Category:Zakieh Sadat Hashemifar of Sunnyvale CA (US)]]

[[:Category:Agis Iakovos Mesolongitis of Oakland CA (US)|Agis Iakovos Mesolongitis of Oakland CA (US)]][[Category:Agis Iakovos Mesolongitis of Oakland CA (US)]]

==SENSOR CALIBRATION VALIDATION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17739530 titled 'SENSOR CALIBRATION VALIDATION

==Simplified Explanation==

The abstract discusses techniques for determining the probability that a first sensor is miscalibrated with respect to a second sensor. 

* Computing device receives calibrated extrinsics of a camera to a lidar
* Determines sets of perturbed extrinsics based on the calibrated extrinsics
* Calculates costs for perturbed extrinsics based on image data captured by the camera and lidar data
* Determines a local maxima score for the calibrated extrinsics
* Calculates a probability that the camera is miscalibrated based on Bayes probability and the local maxima score

== Potential Applications ==
This technology could be applied in various industries such as autonomous vehicles, robotics, and surveillance systems where accurate sensor calibration is crucial for reliable performance.

== Problems Solved ==
This technology helps in identifying potential miscalibrations between sensors, which can lead to inaccurate data and faulty decision-making in systems relying on sensor data.

== Benefits ==
The benefits of this technology include improved accuracy in sensor calibration, enhanced reliability of sensor data, and increased overall performance of systems utilizing multiple sensors.

== Potential Commercial Applications ==
One potential commercial application of this technology could be in the automotive industry for self-driving cars, where precise sensor calibration is essential for safe and efficient operation.

== Possible Prior Art ==
One possible prior art could be the use of Bayesian probability in sensor calibration techniques, although the specific method described in this patent application may be novel.

=== Unanswered Questions ===

=== How does this technology compare to existing sensor calibration methods in terms of accuracy and efficiency? ===
This article does not provide a direct comparison with existing sensor calibration methods, leaving the reader wondering about the potential advantages or limitations of this new approach.

=== What are the potential limitations or challenges in implementing this technology in real-world applications? ===
The article does not address the practical challenges or limitations that may arise when implementing this technology in different industries or environments, leaving room for further exploration and discussion.


==Original Abstract Submitted==

Techniques for determining a probability that a first sensor is miscalibrated with respect a second sensor are discussed herein. For example, a computing device may receive calibrated extrinsics of a camera to a lidar, determine a plurality of sets of perturbed extrinsics based on the calibrated extrinsics, determine respective costs for perturbed extrinsics of the plurality of sets of perturbed extrinsics based on image data captured by the camera, the plurality of sets of perturbed extrinsics, and lidar data captured by the lidar, and determine a local maxima score for the calibrated extrinsics based at least in part on the respective costs for the perturbed extrinsics of the plurality of sets of perturbed extrinsics and a cost of the calibrated extrinsics. The computing device may then determine a probability that the camera is miscalibrated based on a Bayes probability and the local maxima score.



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