Robot Localization via Signal Distribution Mapping
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Solution Overview
Problem
Existing systems for determining the pose of mobile objects in environments lack accuracy due to rotational variability and signal multipath effects, and often require resources that mobile devices cannot afford or implement, especially in varied terrains and environments with complex signal interactions.
Innovation Solution
A method that decomposes the environment into cells defined by nodes, estimates signal measures based on node values, and uses SLAM algorithms to map and localize the object in real-time, compensating for rotational variability and signal multipath effects without an a priori map, utilizing data from both signal and motion sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If known pose determination systems are implemented, then pose estimation capability is provided, but measurement precision deteriorates due to rotational variability and signal multipath effects
Solution Approach 1:
The environment is decomposed into discrete cells with defined nodes, transforming the continuous pose estimation problem into a discrete grid-based localization problem. This segmentation allows the system to handle rotational variability by associating signal measurements with specific cell nodes rather than continuous coordinates, improving robustness against rotation-induced measurement variations.
Solution Approach 2:
The system performs preliminary mapping of expected signal measures at cell nodes before actual pose estimation. By pre-computing and storing signal characteristics at discrete nodes, the system prepares reference data that compensates for rotational variability and multipath effects, enabling more accurate real-time localization without requiring complex runtime corrections.
2Measurement precision
If resource-intensive pose determination systems are used, then measurement precision improves, but device complexity increases beyond mobile device capabilities
Solution Approach 1:
By dividing the environment into cells and nodes, the system reduces the computational complexity of pose estimation. Instead of processing continuous spatial data, the mobile device only needs to determine which discrete cell it occupies and interpolate between node values, significantly reducing computational requirements while maintaining acceptable accuracy.
Solution Approach 2:
The system uses simplified linear interpolation between node values rather than complex probabilistic models. This approximate method consumes fewer computational resources and can be executed on mobile devices with limited processing power, trading minor precision loss for significant reductions in device complexity and energy consumption.
3Productivity
If a priori environmental maps are used, then localization speed improves, but adaptability deteriorates when encountering varied terrains and environmental features
Solution Approach 1:
The system dynamically adapts to environmental variations by allowing cell definitions and node positions to be adjusted based on actual terrain and signal characteristics encountered during operation. Rather than using a fixed static map, the grid structure can be reconfigured to match the actual environment, maintaining both fast localization and high adaptability to varied terrains and signal interactions.
Data Source
AI summary
A robot having a signal sensor configured to measure a signal, a motion sensor configured to measure a relative change in pose, a local correlation component configured to correlate the signal with the position and/or orientation of the robot in a local region including the robot's current position, and a localization component configured to apply a filter to estimate the position and optionally the orientation of the robot based at least on a location reported by the motion sensor, a signal detected by the signal sensor, and the signal predicted by the local correlation component. The local correlation component and/or the localization component may take into account rotational variability of the signal sensor and other parameters related to time and pose dependent variability in how the signal and motion sensor perform. Each estimated pose may be used to formulate new or updated navigational or operational instructions for the robot.