Method for creating an environment map, self-propelled mobile appliance, and computer program
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Solution Overview
Problem
Existing environment mapping methods for mobile self-propelled appliances like robot vacuum cleaners suffer from distortions due to ground unevenness, pitching movements, and varying illumination, leading to inconsistent and low-quality map representations.
Innovation Solution
A method involving weighted averaging of multiple camera images from different directions and lighting conditions, combined with sensor data to create a stable and coherent environment map, reducing distortions and illumination-dependent brightness changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If camera images are transformed assuming a flat floor and unchanged robot orientation, then the transformation process is simple and fast, but ground unevenness and pitching movements cause distortions in the map images
Solution Approach 1:
The patent changes the transformation parameters by introducing robot orientation data and ground unevenness compensation factors. Instead of assuming fixed parameters (flat floor, unchanged orientation), the system dynamically adjusts transformation parameters based on actual robot state and terrain conditions, resolving the contradiction between simple transformation and high-quality mapping
Solution Approach 2:
The system uses feedback from sensors (odometry, inclination sensors) to continuously adjust the transformation process. By feeding back actual robot position, orientation, and ground slope information, the system compensates for distortions in real-time, maintaining high map image quality without excessive complexity
2Productivity
If newly recorded image representations simply replace existing ones, then the map is updated continuously, but image quality deteriorates or fluctuates when new images have lower quality
Solution Approach 1:
The patent introduces quality parameters and weighting factors to control the replacement process. Instead of binary replace/keep decisions, the system uses quality metrics to determine weighting in blending operations, allowing continuous updates while maintaining quality consistency through parameter-based control
Solution Approach 2:
The system creates composite image representations by blending multiple image sources (newly recorded images, existing map data, virtual lighting contributions) with different weightings. This composite approach allows the map to benefit from frequent updates while filtering out low-quality contributions, resolving the contradiction between update frequency and quality consistency
3Manufacturing precision
If multiple versions of identical segments with different qualities are replaced by higher quality versions, then image quality is improved, but visible fluctuations in image content (brightness) occur
Solution Approach 1:
The patent merges multiple image versions using weighted blending instead of simple replacement. By combining multiple sources with appropriate weightings based on their quality and consistency, the system improves overall image quality while maintaining continuity and avoiding visible fluctuations in brightness and content
Solution Approach 2:
The system uses parameter-based control (weighting factors, quality thresholds, blending ratios) to manage the transition between different image versions. These parameters ensure that quality improvements are achieved smoothly without causing abrupt changes or visible fluctuations in the final map output
4Ease of operation
If camera images are used to create illustrated maps, then user recognition of real locations is improved, but changing illumination states and reflections cause negative effects on the map
Solution Approach 1:
The patent introduces virtual lighting models and illumination compensation algorithms as intermediaries between the camera images and the final map. These intermediaries process the raw images to remove illumination-dependent artifacts while preserving the structural information needed for user recognition, effectively filtering out harmful illumination variations
Data Source
AI summary
An environment map is created of a surrounding area for the operation of a mobile self-propelled appliance, such as a floor cleaning appliance being a robot vacuum cleaner and/or robot sweeper and/or mopping robot, by: Capturing images of a floor area of a grid cell of the surrounding area using a camera of the appliance; transforming the images to generate bird's eye view first image representations; combining the first image representations with weighted averaging and inserting a first averaged image representation of the floor area into the environment map, including the appliance position and orientation; capturing an nth image of the floor area of the grid cell and transforming the nth image into a second image representation; combining the first and second image representations of the grid cell while performing weighted averaging of all image representations; and overwriting the first averaged image representation in the environment map with a second averaged image representation.