Offline SLAM Location Determination Using Sensor Processor Batching
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Solution Overview
Problem
Modern computing devices face battery drain and inaccurate location computations when using main application processors for continuous location determination, especially in situations like indoors where GPS signals are unavailable, and existing methods are slow and inefficient.
Innovation Solution
Implementing a sensor processor to collect and batch sensor data periodically, using less power than the main application processor, and performing a simultaneous localization and mapping (SLAM) algorithm optimization using stored data when requested, allowing for accurate and quick location determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the main application processor is used to continuously determine location, then location accuracy is improved, but battery consumption increases
Solution Approach 1:
The system segments the location determination task between two processors: the sensor processor handles continuous sensor data collection and preliminary processing, while the main application processor performs intensive SLAM computations only when location is requested. This division allows accurate location determination without continuous main processor operation, reducing battery consumption while maintaining location accuracy.
Solution Approach 2:
The sensor processor performs preliminary actions by continuously collecting and batching sensor data in the background before a location request is made. This preliminary data preparation enables the main application processor to quickly compute accurate location using pre-collected data, avoiding the need for continuous high-power processing while maintaining both accuracy and efficiency.
2Measurement precision
If the main application processor is used for location determination, then computation accuracy is improved, but processing speed decreases
Solution Approach 1:
Sensor data is continuously collected and batched in advance by the sensor processor before a location request is made. When location computation is needed, the main application processor receives pre-prepared data and performs SLAM algorithm optimization, significantly reducing processing time while maintaining computation accuracy through the use of comprehensive sensor data.
Solution Approach 2:
The system replaces continuous mechanical processing (main processor constantly running) with an event-driven approach where the main processor activates only when location is requested. The sensor processor acts as an intermediary that prepares data in advance, substituting the need for continuous high-power processing with efficient batch processing.
3Measurement precision
If sensor data is collected at high frequency, then location accuracy is improved, but power consumption increases
Solution Approach 1:
The system segments data collection from data processing: the sensor processor continuously collects sensor data at high frequency without waking the main application processor. This segmentation allows high-frequency data collection for accuracy while the main processor remains in low-power state, consuming minimal energy until location computation is actually needed.
Solution Approach 2:
The sensor processor acts as an intermediary between the sensors and the main application processor. It continuously monitors and collects sensor data at high frequency, then batches and transfers this data to the main processor only when needed. This intermediary role enables high-frequency collection without the power penalty of continuous main processor operation.
Data Source
AI summary
Examples describe systems and methods for iteratively determining a signal strength map for a wireless access point (AP) aligned to position coordinates. An example method includes receiving logs of data from devices. For a plurality of iterations, the method includes selecting a set of logs of data having an amount of GPS being less than a given amount of GPS in a previously selected set, determining estimates of signal strength maps for the wireless AP aligned to position coordinates based on the selected set and on given signal strength maps due to a previous iteration, and performing a simultaneous localization and mapping (SLAM) optimization of the possible locations of the wireless AP based on the given signal strength maps and the estimates of the signal strength maps. Based on the iterative optimizations, an output signal strength map is provided for the wireless AP aligned to position coordinates.