Overhead Obstruction Detection Using Point Cloud Segmentation
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Solution Overview
Problem
Current autonomous driving techniques face challenges in accurately detecting overhead obstructions, such as bridges or tunnel ceilings, which can block GPS signals, leading to navigation issues in GPS-denied environments.
Innovation Solution
A method and apparatus that utilize a depth sensing device to collect three-dimensional point cloud data along a path segment, identify data points above the ground plane, project them onto a plane, and detect overhead obstructions by analyzing concentration within cells, storing the detected obstructions in a map database for navigation purposes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If overhead obstructions are detected using traditional sensor systems, then GPS-denied environments can be identified, but false identifications of obstructions may occur reducing detection reliability
Solution Approach 1:
The detection space is segmented into multiple cells arranged in a grid pattern above the ground plane. Each cell independently analyzes point cloud data concentration to determine obstruction presence. This segmentation allows the system to distinguish between localized point cloud variations and actual overhead obstructions, reducing false identifications while maintaining reliable detection.
2Area of stationary object
If point cloud data is collected along the entire path segment, then complete coverage is achieved, but data processing complexity increases
Solution Approach 1:
The path segment detection area is divided into multiple cells forming a grid structure. This segmentation transforms the complex task of analyzing entire point cloud data into simpler cell-level concentration analysis. Each cell independently processes only the point cloud data within its boundaries, significantly reducing overall processing complexity while maintaining complete area coverage.
Solution Approach 2:
The three-dimensional point cloud data is projected onto a two-dimensional plane and then discretized into cells. This dimensional reduction and discretization transforms continuous spatial data into a structured grid format, making the data more manageable and easier to process while preserving the complete detection coverage area.
3Measurement precision
If GPS signals are used for navigation, then positioning accuracy is maintained, but overhead obstructions block signals causing navigation failures
Solution Approach 1:
The system performs preliminary detection of overhead obstructions by analyzing point cloud data concentration in cells before GPS signal failure occurs. By proactively identifying areas with dense point cloud data that indicate overhead structures, the system can prepare alternative navigation methods in advance, ensuring continuous reliable navigation even when GPS signals are blocked.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution enables proactive identification of areas with poor GPS visibility, allowing vehicles to switch navigation methods and preventing false identifications of obstructions, thereby enhancing navigation accuracy and safety.
Implementation Method 1
The height pole detection system can include a laser operable to emit a plurality of laser light pulses substantially upwards and can enable detection of the height of the overhead objects based on a time of flight of detected laser light pulses.
Implementation Method 2
US2015338524A1 discloses methods for improving a position determined using a receiver of a global navigation satellite systems. In some embodiments, information regarding obstructing objects such as utility poles, navigation signs, and bridges, is obtained from 3D Geographic Information Systems (GIS) and/or from Light Detection and Ranging (LIDAR) photographs.
Data Source
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AI summary
Methods, apparatuses, and systems are provided for detecting overhead obstructions along a path segment. One exemplary method includes receiving three-dimensional data collected by a depth sensing device traveling along a path segment, wherein the three-dimensional data comprises point cloud data positioned above a ground plane of the path segment. The method further includes identifying data points of the point cloud data positioned within a corridor positioned above the ground plane. The method further includes projecting the identified data points onto a plane. The method further includes detecting the overhead obstruction based on a concentration of point cloud data positioned within a plurality of cells of the plane. The method further includes storing the detected overhead obstruction above the path segment within a map database.