Autonomous Vehicle SLAM Using Boundary Distance Sensing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing autonomous vehicles, such as lawn mowers and scarifiers, face inefficiencies in outdoor environments due to unstable SLAM results from environmental sensors like cameras, which are affected by changing lighting conditions and seasonal changes, leading to random movement patterns that result in uneven area coverage.
Innovation Solution
Incorporating a boundary distance sensing means, specifically electromagnetic field sensors, to provide a stable and robust distance signal that complements data from environmental sensors, allowing the computing unit to improve SLAM performance by correlating distance signals with boundary indications, thereby enhancing mapping and localization capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If laser scanners are used to improve SLAM results, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent combines multiple sensor types (electromagnetic field sensors for boundary detection, cameras for visual navigation, odometry sensors for position tracking) into an integrated sensing system. The computing unit fuses data from all these sensors to achieve robust SLAM performance without relying on expensive laser scanners alone.
Solution Approach 2:
The electromagnetic field sensors serve multiple functions: they detect boundary wires, provide position information, and contribute to map generation. This multi-functional approach replaces the need for dedicated expensive sensors while maintaining SLAM accuracy.
2Device complexity
If cameras are used for SLAM in outdoor environments, then device complexity is reduced, but reliability deteriorates due to changing lighting conditions
Solution Approach 1:
The electromagnetic field sensors act as an intermediary reference system that provides stable position information independent of lighting conditions. This intermediary data source compensates for the instability of camera-based SLAM in outdoor environments by providing a reliable boundary detection mechanism.
Solution Approach 2:
The system changes the sensing parameters by incorporating electromagnetic field detection alongside optical sensing. This multi-parameter approach allows the system to adapt to varying lighting conditions by relying more on electromagnetic field data when optical conditions are poor.
3Ease of operation
If random movement patterns are used for area coverage, then ease of operation is improved, but productivity deteriorates due to uneven coverage
Solution Approach 1:
The system implements feedback through continuous boundary detection and position tracking using electromagnetic field sensors. The computing unit uses this feedback to adjust the vehicle's movement pattern from random to systematic, ensuring complete and efficient area coverage while maintaining autonomous operation.
Solution Approach 2:
The vehicle's movement pattern dynamically adapts based on real-time position information from electromagnetic field sensors. The system transitions from static random movement to dynamic systematic navigation, optimizing area coverage efficiency while preserving ease of autonomous operation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly stabilizes and robustifies SLAM results for outdoor applications, enabling more efficient area coverage by ensuring accurate mapping and localization, even under varying environmental conditions, and allows for direct path planning to charging stations and areas requiring additional mowing.
Implementation Method 1
at least one boundary distance sensing means, in particular at least one electromagnetic field sensor, for generating a distance signal which is correlated to a distance between the autonomous vehicle and a boundary indication means
Data Source
Figure 1~2
Figure 3
Figure 4A~4C
AI summary
The invention relates to an autonomous vehicle comprising a driving means and a system including such autonomous vehicle. The autonomous vehicle furthermore comprises at least one environment sensing means (10, 11) for sensing an environment of the autonomous vehicle (1). It furthermore comprises a computing unit (7) configured to perform a mapping function and a localization function. The mapping function is performed on the basis of respective signals supplied from the at least one environment sensing means (10, 11) to build up a map. The localization function localizes the autonomous vehicle (1) within the map and generates respective localization information, The autonomous vehicle (1) further comprises a boundary distance sensing means (12, 13) configured to generate a distance signal correlated to a distance between the autonomous vehicle and a boundary indication means (15). The computing unit (7) is configured to receive the distance signal and to perform at least one of the mapping function and the localization function on the basis of a signal from the at least one environment sensing means (10, 11) and the distance signal from the boundary distance sensing means (12, 13). The system comprises in addition to the autonomous vehicle a boundary wire indicating a border of an entire area in which autonomous driving of the autonomous vehicle (1) shall be performed.