17946415. ITERATIVE HISTOGRAM BINWIDTH OPTIMIZATION METHOD FOR A LIDAR SYSTEM AND LIDAR SYSTEM IMPLEMENTING SAME simplified abstract (Huawei Technologies Co., Ltd.)
Contents
- 1 ITERATIVE HISTOGRAM BINWIDTH OPTIMIZATION METHOD FOR A LIDAR SYSTEM AND LIDAR SYSTEM IMPLEMENTING SAME
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ITERATIVE HISTOGRAM BINWIDTH OPTIMIZATION METHOD FOR A LIDAR SYSTEM AND LIDAR SYSTEM IMPLEMENTING SAME - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
ITERATIVE HISTOGRAM BINWIDTH OPTIMIZATION METHOD FOR A LIDAR SYSTEM AND LIDAR SYSTEM IMPLEMENTING SAME
Organization Name
Inventor(s)
Ali Ahmed Ali Massoud 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 17946415 titled 'ITERATIVE HISTOGRAM BINWIDTH OPTIMIZATION METHOD FOR A LIDAR SYSTEM AND LIDAR SYSTEM IMPLEMENTING SAME
Simplified Explanation
The abstract describes a LIDAR system and method for detecting objects using an iterative process. The system acquires data points, organizes them into bins, identifies target bins, determines object distances, and reduces data points for further analysis.
- LIDAR system with controller for acquiring data points and performing iterative process
- First iteration determines number of bins, organizes data points, identifies target bin, and calculates distance of first object
- Second iteration uses reduced data points to determine distance of second object
Potential Applications
The technology can be applied in autonomous vehicles for object detection and collision avoidance systems, robotics for navigation and mapping, and industrial automation for object tracking and monitoring.
Problems Solved
The technology solves the problem of accurately detecting and measuring distances to objects in various environments, improving the efficiency and safety of systems relying on object detection.
Benefits
The benefits of this technology include enhanced accuracy in object detection, improved reliability in distance measurements, and increased efficiency in processing data for real-time applications.
Potential Commercial Applications
The technology can be commercially applied in automotive industry for advanced driver assistance systems, in agriculture for precision farming applications, and in security systems for perimeter monitoring and intrusion detection.
Possible Prior Art
One possible prior art could be the use of LIDAR systems in autonomous vehicles for object detection and mapping, which has been a growing field of research and development in recent years.
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, leaving the reader to wonder about the relative performance of this technology.
What are the potential limitations or challenges faced in implementing this technology in real-world applications?
The article does not address any potential limitations or challenges that may arise when implementing this technology, leaving room for further exploration of practical considerations.
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.
