GNSS SLAM 3-D Mapping via SNR Probabilistic Localization
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Solution Overview
Problem
Global Navigation Satellite Systems (GNSS) localization quality is degraded in urban areas due to signal blockage and multi-path reflections, and existing 3-D mapping methods are expensive and prone to self-localization errors, requiring advanced surveying techniques like LiDAR or aerial photography.
Innovation Solution
A probabilistic approach using GNSS signal-to-noise ratio (SNR) measurements for simultaneous localization and mapping (SLAM), employing Belief Propagation to construct a 3-D map by assigning likelihoods to GNSS signal rays and stitching them together, reducing computational complexity and enabling online and offline mapping scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR or aerial photography surveying is used for 3-D mapping, then mapping accuracy is improved, but cost and complexity increase significantly
Solution Approach 1:
The patent replaces complex mechanical surveying systems (LiDAR, aerial photography equipment) with a computational approach using standard GNSS receivers. Instead of using active sensing hardware to directly measure 3-D space, the system uses passive GNSS signal measurements combined with probabilistic algorithms (belief propagation) to infer environmental geometry and create 3-D maps, thereby substituting mechanical complexity with computational processing
Solution Approach 2:
The patent creates a computational model (probabilistic 3-D map) that copies the essential geometric structure of the physical environment. Rather than directly measuring physical space with complex instruments, the system infers and reconstructs the environmental model from indirect GNSS signal observations, producing a simplified yet functional representation of 3-D space
2Measurement precision
If LiDAR or aerial photography is used for 3-D mapping, then mapping precision is improved, but loss of time and operational difficulty increase
Solution Approach 1:
The system performs simultaneous localization and mapping using only the GNSS receiver's own measurements, without requiring external surveying equipment or pre-existing map data. The probabilistic SLAM algorithm enables the system to self-determine its position and concurrently build the environmental model from scratch using only the signals it receives, making the process autonomous and eliminating the need for time-consuming external surveying operations
3Productivity
If probabilistic SLAM with belief propagation is used, then computational efficiency is improved, but measurement precision may be reduced compared to direct sensing
Solution Approach 1:
The patent transforms the localization problem from direct geometric calculation to probabilistic parameter estimation. By representing the environment as a probabilistic map with occupancy probabilities and using belief propagation to estimate parameters (position, map structure) from indirect SNR measurements, the system achieves computational efficiency through iterative optimization while maintaining reasonable precision through probabilistic reasoning
Data Source
AI summary
Various embodiments each include at least one of systems, methods, devices, and software for GNSS simultaneous localization and mapping (SLAM). The disclosed techniques demonstrate that simultaneous localization and mapping (SLAM) can be performed using only GNSS SNR and geo-location data, collectively termed GNSS data henceforth. A principled Bayesian approach for doing so is disclosed. A 3-D environment map is decomposed into a grid of binary-state cells (occupancy grid) and the receiver locations are approximated by sets of particles. Using a large number of sparsely sampled GNSS SNR measurements and receiver/satellite coordinates (all available from off-the-shelf GNSS receivers), likelihoods of blockage are associated with every receiver-to-satellite beam. Loopy Belief Propagation is used to estimate the probabilities of each cell being occupied or empty, along with the probability of the particles for each receiver location.