Polar-to-Cartesian Object Detection for Asynchronous Sensor Fusion
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Solution Overview
Problem
Existing object detection systems face challenges in fusing data from multiple sensors of mixed modality without a coherent frame of reference, leading to high computational and memory requirements, loss of information, and inaccurate object detection due to asynchronous sensor data processing.
Innovation Solution
An object detection system that uses sensor encoding neural networks to generate polar feature vectors from sensor POV data, which are then mapped and concatenated with offset vectors to create transformed tensors, processed by convolutional neural networks to update a cartesian representation of the region of interest, allowing for efficient data compression and adaptive mapping without requiring sensor synchronization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If raw data from multiple sensors is fused and processed to obtain information about objects, then object detection accuracy is improved, but computational, memory, and power resources are significantly increased
Solution Approach 1:
The system segments the sensor data processing by first encoding each sensor's data into a compressed polar representation independently, then mapping these to a common cartesian frame. This segmentation allows each sensor to be processed separately before fusion, reducing the computational burden compared to processing all raw sensor data simultaneously while maintaining detection accuracy.
Solution Approach 2:
The patent extracts essential information from raw sensor data by encoding it into a compressed polar representation that captures only the necessary features for object detection. This extraction process filters out redundant information and reduces data volume before the fusion stage, thereby reducing computational and memory requirements while preserving detection accuracy.
2Productivity
If sensor data is processed individually at object creation stage then fused, then processing efficiency is improved, but much information about objects is lost
Solution Approach 1:
The system performs preliminary encoding of sensor data into polar representations that preserve essential object information before the fusion stage. This preliminary action ensures that when data is processed individually and then fused, the information is already organized in a way that minimizes loss, allowing efficient processing while maintaining data integrity.
Solution Approach 2:
The patent changes the representation parameters from raw sensor coordinates to polar coordinates (radius, angle, direction) which are more compact and information-preserving. This parameter transformation allows efficient processing while maintaining the necessary object information, as the polar representation captures geometric features that are invariant to coordinate system changes.
3Adaptability or versatility
If multiple sensors of mixed modality are used to detect objects, then detection capability is improved, but there is no obvious coherent way to fuse the raw data
Solution Approach 1:
The patent introduces a polar representation as an intermediary format that mediates between different sensor modalities. Each sensor encodes its data into this common polar language, which then serves as the basis for fusion in the cartesian frame. This intermediary representation simplifies the fusion process by providing a unified structure that can accommodate data from different sensor types without requiring complex modality-specific fusion algorithms.
4Reliability
If sensor data is processed without synchronization, then system robustness is improved, but accurate object detection becomes difficult
Solution Approach 1:
The system dynamically handles asynchronous sensor data by encoding each sensor's measurements into polar representations that can be independently processed and then mapped to a common cartesian frame at different times. This dynamic approach allows the system to maintain robustness to synchronization issues while achieving accurate detection through the flexible polar-to-cartesian transformation that adapts to varying timing conditions.
Data Source
AI summary
A method for updating a cartesian representation includes receiving a sensor POV data based on a sensor data generated by at least one sensor for a plurality of view cones and generating a polar feature vector based on the sensor POV data. The polar feature vector includes a plurality of cone vectors corresponding to the plurality of view cones. The method further includes mapping at least one cell from a plurality of cells of the cartesian representation with at least one cone vector from the plurality of cone vectors, receiving at least one offset vector corresponding to the at least one cell, concatenating at least the at least one cone vector and the at least one offset vector to generate at least one transformed tensor for the at least one cell, and generating at least one learned output based on the at least one transformed tensor.