Client-Server Location Estimation with Motion Summarizer
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Solution Overview
Problem
Existing location estimation techniques using GNSS trilateration suffer from significant latency, especially for moving clients, and are prone to errors in urban environments due to signal reflections, leading to inaccurate location estimates and connectivity issues when disconnected from servers.
Innovation Solution
A location estimation system that distributes functionality between client and server devices, utilizing a motion summarizer on the client and a position estimator on the server, allowing for client-side location estimation and 'rewind' techniques to incorporate server estimates upon reconnection, and employing alternative methods like Bluetooth beacons for disconnected scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If location estimation is performed exclusively on the server using particle filter techniques, then measurement precision is improved, but loss of time increases due to network latency and processing delays
Solution Approach 1:
The location estimation system is segmented into two independent components: a client-side motion summarizer that performs lightweight processing locally, and a server-side position estimator that performs comprehensive particle filter analysis. This segmentation allows the client to generate immediate location estimates with acceptable precision while the server simultaneously processes more accurate estimates, resolving the contradiction between precision and latency by providing multiple estimation streams at different accuracy levels.
Solution Approach 2:
The motion summarizer on the client device performs preliminary location estimation using inertial measurements and previous position data before server processing completes. This preliminary action provides immediate location feedback to the user while the more accurate server-side particle filter estimation is being computed, effectively reducing the perceived latency while maintaining high measurement precision through the eventual server result.
2Loss of time
If particle filter processing is moved to the client device to reduce latency, then loss of time is reduced, but device complexity increases beyond client capabilities
Solution Approach 1:
The client device performs a partial version of location estimation using the motion summarizer, which applies simplified algorithms to inertial measurements and previous position data. This partial action provides sufficient location information for immediate use without requiring the full computational power of a particle filter, thus reducing latency while staying within client device capabilities. The server then performs the complete particle filter analysis for enhanced accuracy when needed.
3Measurement precision
If location estimation relies exclusively on server connection, then measurement precision is improved through comprehensive processing, but reliability decreases during network disconnection
Solution Approach 1:
The client device is equipped with self-service location estimation capability through the motion summarizer, which can independently process inertial measurements and generate location estimates without requiring server connection. This self-service mechanism ensures continuity of location estimation during network disconnection, tunnel entry, or server unavailability, while the server-side particle filter continues to provide enhanced precision when connected, thus resolving the contradiction between precision and reliability.
4Ease of operation
If GNSS trilateration is used for location estimation, then ease of operation is maintained, but measurement precision deteriorates in urban environments due to signal reflections
Solution Approach 1:
The system merges multiple location estimation approaches: traditional GNSS trilateration for open environments, inertial-based motion summarization for continuous tracking, and server-side particle filter processing for enhanced accuracy in challenging environments. This combination maintains the simplicity of GNSS operation while compensating for its precision limitations in urban canyons through the complementary strengths of inertial and probabilistic estimation methods.
Data Source
AI summary
A client device maintains location state data including a first location estimate of a geographic location of the client device. The first location estimate is based on a first motion measurement obtained over a first time period. The client device retrieves a second motion measurement obtained over a second time period subsequent to the first time period and uses it and the first location estimate to generate a second location estimate. The client device sends the second location estimate to a server. The server further processes the second location estimate to generate an updated second location estimate. The client device retrieves a third motion measurement and generates a third location estimate. The client device receives the updated second location estimate and uses it to adjust the third location estimate. A fourth location estimate is generated using the adjusted third location estimate.