View source for Huawei technologies co., ltd. (20240094359). ITERATIVE HISTOGRAM BINWIDTH OPTIMIZATION METHOD FOR A LIDAR SYSTEM AND LIDAR SYSTEM IMPLEMENTING SAME simplified abstract

=ITERATIVE HISTOGRAM BINWIDTH OPTIMIZATION METHOD FOR A LIDAR SYSTEM AND LIDAR SYSTEM IMPLEMENTING SAME=


==Organization Name==

[[:Category:huawei technologies co., ltd.|huawei technologies co., ltd.]]


[[Category:huawei technologies co., ltd.]]

==Inventor(s)==

[[:Category:Ali Ahmed Ali Massoud of Kanata (CA)|Ali Ahmed Ali Massoud of Kanata (CA)]][[Category:Ali Ahmed Ali Massoud of Kanata (CA)]]

[[:Category:Zhiping Jiang of Kanata (CA)|Zhiping Jiang of Kanata (CA)]][[Category:Zhiping Jiang of Kanata (CA)]]

==ITERATIVE HISTOGRAM BINWIDTH OPTIMIZATION METHOD FOR A LIDAR SYSTEM AND LIDAR SYSTEM IMPLEMENTING SAME - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240094359 titled 'ITERATIVE HISTOGRAM BINWIDTH OPTIMIZATION METHOD FOR A LIDAR SYSTEM AND LIDAR SYSTEM IMPLEMENTING SAME

==Simplified Explanation==

The patent application describes a lidar system and method for detecting objects using an iterative process to analyze data points.

* The lidar system acquires data points representing detected signals and organizes them into bins.
* The system identifies a target bin and determines the distance of an object based on that bin.
* A reduced set of data points is then used to determine the distance of a second object during a second iteration.

== Potential Applications ==
This technology can be applied in autonomous vehicles for object detection and collision avoidance systems.

== Problems Solved ==
This technology helps in accurately detecting and measuring distances to objects in the environment, improving safety in various applications.

== Benefits ==
The lidar system provides precise and reliable object detection capabilities, enhancing the overall performance of autonomous systems.

== Potential Commercial Applications ==
Commercial applications include autonomous vehicles, robotics, surveillance systems, and industrial automation.

== Possible Prior Art ==
Prior lidar systems may have used similar iterative processes for object detection, but this specific method of organizing data points into bins and reducing them for subsequent iterations may be novel.

== Unanswered Questions ==
=== How does this technology compare to other object detection methods in terms of accuracy and efficiency? ===
This article does not provide a direct comparison with other object detection methods, so it is unclear how this technology stacks up against alternatives.

=== What are the limitations of this lidar system in terms of environmental conditions or types of objects it can detect? ===
The article does not address any potential limitations of the lidar system, leaving uncertainty about its performance in challenging conditions or with certain types of objects.


==Original Abstract Submitted==

a lidar system and a method for detecting objects are disclosed. the lidar system has a controller configured to acquire a plurality of data points representative of detected signals, and perform an iterative process. during the first iteration the controller is configured to determine a first number of bins based on the plurality of data points, and in response to the first number being above a threshold: (i) organize the plurality of data points into the first number of bins, (ii) identify a target bin amongst the first number of bins, (iii) determine the distance of a first object based on the target bin; and (iv) determine a reduced plurality of data points based on the plurality of data points, which excludes data points associated with the target bin. during a second iteration, the controller determines a distance of a second object based on the reduced plurality of data points.



[[Category:G01S7/4865]]
[[Category:G01S17/42]]