Robotic Sensor Packet Processing for Low-Latency Spatial Mapping
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Solution Overview
Problem
Existing robotic systems face challenges in accurately and efficiently updating their spatial memory to perceive and navigate their environment due to latency issues and computational inefficiencies in processing sensor data.
Innovation Solution
A computing system processes sensor data packets in a streaming manner using machine-learned models to generate local feature maps, which are then used to update a spatial map, incorporating local features and enabling object detection in a robotic platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data packets are processed in a traditional batch manner to ensure accurate object detection, then measurement precision is improved, but loss of time increases due to latency in processing
Solution Approach 1:
The patent segments the sensor data processing into incremental updates based on individual sensor data packets rather than waiting for complete batches. Each packet triggers an incremental update to the spatial map, allowing the system to process data as it arrives and reduce latency while maintaining detection accuracy through cumulative processing of segmented data portions.
2Measurement precision
If traditional spatial memory updating methods are used to ensure complete environmental perception, then measurement precision is improved, but use of energy increases due to computational inefficiencies
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor data packets to extract relevant features before they are needed for complete environmental perception. Local feature maps are generated in advance from individual packets and stored for later integration, reducing the computational burden during real-time processing and lowering energy consumption while maintaining perception accuracy.
3Productivity
If incremental processing of sensor data packets is implemented to reduce latency, then productivity is improved, but measurement precision may deteriorate due to incomplete data processing
Solution Approach 1:
The system implements feedback mechanisms where the spatial map is continuously updated with local features from each processed packet, and these updates are fed back into subsequent processing steps. This feedback loop ensures that even though processing is incremental, the accumulated information from multiple packets progressively improves detection accuracy, preventing precision deterioration while maintaining high productivity.
Data Source
AI summary
Systems and methods for streaming sensor packets in real-time are provided. An example method includes obtaining a sensor data packet representing a first portion of a three-hundred and sixty degree view of a surrounding environment of a robotic platform. The method includes generating, using machine-learned model(s), a local feature map based at least in part on the sensor data packet. The local feature map is indicative of local feature(s) associated with the first portion of the three-hundred and sixty degree view. The method includes updating, based at least in part on the local feature map, a spatial map to include the local feature(s). The spatial map includes previously extracted local features associated with a previous sensor data packet representing a different portion of the three-hundred and sixty degree view than the first portion. The method includes determining an object within the surrounding environment based on the updated spatial map.