Robot Landmark Localization Using Candidate Area State Estimation
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Solution Overview
Problem
Autonomous robots using SLAM struggle to estimate their position accurately due to the limited observable range and directivity of sensors like RGB cameras and range finders, leading to increased processing complexity and potential instability in navigation.
Innovation Solution
A moving object controller that includes an observation obtaining unit, a landmark detection unit, a candidate area obtaining unit, and a state estimation unit, which uses distance and angle information from multiple landmarks to narrow down candidate positions and improve state estimation accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an RGB camera or range finder is used to detect landmarks, then observation data can be obtained, but the limited observable range and directivity result in only a small number of landmarks being detected at a time, reducing position estimation accuracy
Solution Approach 1:
The patent combines multiple types of sensors (RGB camera, infrared camera, range finder) to detect landmarks simultaneously. This merging of sensing capabilities allows the system to overcome the limited observable range of individual sensors and detect more landmarks at once, thereby improving position estimation accuracy without relying on a single sensor type.
2Reliability
If multiple candidate positions are considered for robot localization, then position estimation completeness is improved, but the amount of processing increases significantly, reducing estimation speed
Solution Approach 1:
The patent extracts and utilizes invariant features from landmark appearances that remain consistent across different viewing conditions. By focusing on these extracted invariant features rather than processing all possible candidate positions comprehensively, the system maintains reliable position estimation while significantly reducing the computational burden and improving estimation speed.
3Device complexity
If only distance information from landmarks is used for position estimation, then the system is simpler to implement, but the position accuracy is insufficient because all points on a circle centered on the landmark can be candidates
Solution Approach 1:
The patent transitions from using only distance information (one-dimensional constraint forming a circle) to incorporating appearance information of landmarks (adding visual dimension). By matching the observed appearance of landmarks with stored landmark data, the system creates additional constraints that narrow down candidate positions from a circular range to specific discrete locations, significantly improving position estimation accuracy.
Data Source
AI summary
The position of a moving object is estimated with high accuracy using landmark information, and highly accurate state estimation is performed appropriately at high speed. A landmark detection unit obtains a distance between the moving object and each of two or more landmarks as landmark distance information based on observation data obtained by an observation obtaining unit. A candidate area obtaining unit determines a candidate area for a position of the moving object based on the landmark distance information obtained by the landmark detection unit, and obtains candidate area information indicating the determined candidate area. A state estimation unit estimates an internal state of the moving object based on the observation data, the landmark distance information, and the candidate area information to obtain moving object internal state estimation data, and estimates the environmental map based on the candidate area information and the landmark distance information to obtain environmental map data.