3D Object Detection Using Sensor Data Diversity
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Solution Overview
Problem
Current object detection systems in robotics and autonomous systems are limited by their reliance on two-dimensional (2D) planar laser rangefinders or lidar, which struggle to detect objects in three-dimensional (3D) spaces, such as flying or hanging objects, and may require preconfigured algorithms that are not flexible enough for varying detection zone requirements.
Innovation Solution
The system employs multiple analysis techniques for object detection in detection zones, utilizing computational diversity and implementational diversity by running different techniques on various computing platforms. This approach enhances the robustness and accuracy of object detection by allowing for real-time adjustments and improved handling of latency and depth quantization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a 2D planar laser rangefinder or lidar is used to sweep a circular plane, then the detection system can identify objects within the circular plane, but it cannot properly detect objects in 3D spaces such as flying or hanging objects
Solution Approach 1:
The patent transitions from 2D planar laser rangefinders to 3D time-of-flight sensors that can detect objects in three-dimensional space. The sensor array captures depth information across multiple dimensions, enabling detection of flying, hanging, and other objects that exist outside the traditional 2D detection plane. This dimensional expansion resolves the contradiction by maintaining detection precision while significantly improving adaptability to various spatial configurations.
2Productivity
If a particular detection algorithm is preconfigured to calculate a specific set of disparities, then the algorithm can process data efficiently, but it cannot be adjusted at inference time to meet varying detection zone requirements
Solution Approach 1:
The patent implements dynamic adjustment of detection parameters at inference time. The system allows modification of detection zone specifications, disparity calculation parameters, and analysis technique selection based on real-time requirements. This dynamic capability enables the system to adapt to varying detection scenarios while maintaining efficient processing through optimized algorithm selection and parameter configuration.
Solution Approach 2:
The system enables changeable detection parameters including detection zone boundaries, depth ranges, and analysis technique parameters. By allowing parameter adjustment at inference time rather than requiring fixed preconfiguration, the system achieves both flexibility for different detection scenarios and maintained processing efficiency through parameter-optimized algorithm execution.
3Reliability
If multiple analysis techniques are implemented on different computing platforms, then the system achieves greater robustness and diversity in object detection, but the device complexity increases
Solution Approach 1:
The patent divides the detection system into multiple independent analysis techniques that can be executed on different computing platforms. Each technique processes sensor data independently using different algorithms and computational approaches. This segmentation provides robustness through diversity while managing complexity by allowing each segment to be optimized and implemented separately on suitable hardware platforms.
Data Source
AI summary
The present disclosure relates to detecting objects in detection zones using multiple analysis techniques. The multiple analysis techniques may be used to analyze sensor data corresponding to the detection zones. The multiple analysis techniques may be selected based at least on at least two of the analysis techniques of the multiple analysis techniques having a computational diversity by performing different types of computational analyses on the sensor data with respect to each other, and at least two analysis techniques of the multiple analysis techniques having implementation diversity by being implemented on different types of computing platforms with respect to each other.