Sensor Data Segmentation for Ground Removal
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Solution Overview
Problem
Current data segmentation techniques for autonomous vehicles and robotics face challenges in accurately removing the ground surface from sensor data, which hinders efficient object detection and vehicle navigation, particularly in complex environments.
Innovation Solution
The techniques involve using a combination of image-based processing and point-cloud based processing to segment sensor data, employing machine learning models for segmentation information, and removing the ground surface using geometric techniques or segmentation information, followed by clustering to identify objects within a voxel space, allowing for dynamic and static object tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional segmentation techniques are used to process sensor data, then object detection can be performed, but the ground surface removal is inaccurate and memory requirements are high
Solution Approach 1:
The sensor data is segmented into multiple channels (e.g., depth channel, intensity channel, confidence channel) that can be processed independently. This segmentation allows for targeted processing of different data aspects, improving ground surface removal accuracy while reducing the memory burden of processing unified multi-dimensional data blocks.
Solution Approach 2:
The patent transforms the processing approach by treating sensor data as multi-channel two-dimensional images rather than three-dimensional point clouds. This dimensional transformation enables the use of efficient 2D image processing algorithms, significantly reducing memory requirements while maintaining or improving processing accuracy through channel-specific operations.
2Measurement precision
If complex segmentation processing is applied to sensor data, then object detection accuracy improves, but data processing time increases
Solution Approach 1:
By dividing sensor data into separate channels representing different physical quantities (depth, intensity, confidence), the system can apply specialized processing to each channel independently. This enables parallel processing and avoids the computational overhead of processing all data dimensions uniformly, reducing total processing time while improving detection accuracy.
Solution Approach 2:
Different processing techniques are applied to different channels based on their specific characteristics. For example, depth information may be processed differently from intensity information, allowing each channel to be optimized for its specific purpose. This localized processing approach improves overall system efficiency and accuracy without requiring uniform complex processing across all data.
3Ease of operation
If ground surface removal is performed using traditional methods, then processing can be completed, but the distinction between static and dynamic objects is unclear
Solution Approach 1:
The multi-channel segmentation preserves different types of information in separate channels (e.g., depth, intensity, confidence). This separation maintains object type information that would otherwise be lost in traditional ground removal methods, enabling clearer distinction between static and dynamic objects while simplifying the classification process.
Solution Approach 2:
The patent introduces confidence information as an intermediary channel that mediates between raw sensor data and final object classification. This confidence channel provides additional information about data reliability, helping to distinguish between actual objects and ground surface artifacts, thereby improving object classification ease and information retention.
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 approach enhances the efficiency of data processing by reducing memory requirements and simplifying data evaluation, improving object detection and vehicle navigation by accurately distinguishing between static and dynamic objects and generating appropriate trajectories.
Implementation Method 1
receiving sensor data of an environment captured by a time of flight sensor on an autonomous vehicle
Data Source
AI summary
Techniques are discussed for segmenting sensor data captured by a sensor to remove a ground surface from the sensor data. A first technique includes capturing sensor data represented as multichannel image and segmenting the image according to image processing techniques. The ground surface can be removed from the sensor data, and a subset of the sensor data can be associated with a voxel space. A second technique includes capturing sensor data and unprojecting the sensor data to generate three dimensional data, which can be associated with a voxel space. Ground plane data associated with a location can be accessed or determined and voxel data that is within a threshold height of the ground plane data can be removed from the voxel space. Clustering techniques can determine objects represented in the data, and a vehicle can be controlled based on the objects.