Multi-Sensor SLAM Location Estimation With Dynamic Sensor Switching
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Solution Overview
Problem
Simultaneous localization and mapping (SLAM) systems face high calculation loads and power consumption when using multiple sensors, leading to a trade-off between accuracy and reliability, with existing methods either increasing calculation costs or decreasing reliability due to sensor errors and variations.
Innovation Solution
A location estimation system using at least four directional sensors, where two sensors are selectively assigned for location estimation, with the system swapping sensors based on feature points and success rates to maintain reliability and reduce calculation load, ensuring continuous and accurate location estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor information from all of the plurality of sensors is processed, then location estimation accuracy is improved, but calculation load increases significantly
Solution Approach 1:
The patent extracts and processes sensor information selectively rather than using all sensors. The sensor assignment section chooses specific sensors (first sensor and second sensor) from the plurality of sensors based on their suitability, eliminating the need to process information from all sensors while maintaining location estimation accuracy.
Solution Approach 2:
The patent creates a universal sensor assignment mechanism that can adaptively select from multiple sensors. The sensor assignment section evaluates suitability criteria and dynamically assigns appropriate sensors, making the system flexible and multi-functional in handling different sensor configurations while reducing calculation load.
2Device complexity
If switching between sensors of a plurality of sensors is performed, then calculation costs are reduced, but measurement error increases due to single sensor limitations
Solution Approach 1:
The patent merges information from multiple sensors by assigning both a first sensor and a second sensor to contribute to location estimation. The environment map generator combines data from both sensors, and the location integration section integrates estimated locations from both sensors, thereby maintaining measurement reliability while managing calculation costs through selective rather than exhaustive processing.
Solution Approach 2:
The patent changes the parameter of sensor selection by introducing suitability criteria that evaluate sensors based on environmental factors. The sensor assignment section adjusts which sensors are active based on these criteria, dynamically changing sensor parameters to optimize the balance between calculation costs and measurement reliability.
3Device complexity
If information from sensors with low suitability is excluded, then calculation load is reduced, but reliability decreases due to potential sensor malfunction
Solution Approach 1:
The patent prepares for potential sensor malfunction by having a pool of available sensors and a mechanism to reassign them. The sensor assignment section can switch between sensors based on suitability criteria, providing a cushion against reliability issues. If one sensor fails or performs poorly, another sensor can take its place, maintaining system reliability while keeping calculation load manageable.
Data Source
AI summary
A location estimation system includes at least four directional sensors that each acquire data used to estimate a location of a mobile object; a sensor assignment section that assigns one of the at least four sensors as a first sensor, and assigns, as a second sensor, one of the at least four sensors that is adjacent to the first sensor and situated on one of sides of the first sensor; an environment map generator that generates an environment map on the basis of first sensor data that is data acquired by the first sensor and on the basis of second sensor data that is data acquired by the second sensor; a first location estimator that estimates the location in the environment map on the basis of the first sensor data to generate a first sensor estimated location; a second location estimator that estimates the location in the environment map on the basis of the second sensor data to generate a second sensor estimated location; and a location integration section that integrates the first sensor estimated location and the second sensor estimated location to estimate the location of the mobile object in the environment map.