Monocular Auxiliary Sensor Fusion for Fast Robot Positioning
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Solution Overview
Problem
Existing technologies face challenges in providing fast, accurate, and reliable positional awareness for autonomous robots and self-guiding mobile platforms, particularly in recognizing locations and obstructions quickly and efficiently.
Innovation Solution
The use of a monocular-auxiliary sensor system that combines visual processing with data from inertial sensors and wheel odometry, allowing for the estimation of depth and movement magnitude, and calibrating camera-displacement data with auxiliary sensor data to determine the mobile unit's position on a global map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If visual processing approaches are used for positional awareness, then cost is reduced and simplicity is improved, but speed and reliability deteriorate leading to failure in fast motion applications
Solution Approach 1:
The patent combines a monocular camera with auxiliary sensors (inertial measurement unit and/or wheel odometry unit) to create a hybrid positioning system. The visual processing provides cost-effective positional awareness while the auxiliary sensors compensate for the slow processing speed and scale ambiguity, achieving reliable fast-motion positioning without depth sensors.
Solution Approach 2:
The auxiliary sensors act as intermediaries that bridge the gap between monocular visual processing and accurate positional awareness. The inertial measurement unit provides high-frequency motion data while the wheel odometry provides displacement information, both mediating the scale ambiguity and speed limitations of monocular vision.
2Measurement precision
If depth sensors are used for positional awareness, then measurement precision is improved, but cost increases and power consumption increases
Solution Approach 1:
The patent replaces depth sensors (which use active illumination and complex processing) with a combination of monocular vision and auxiliary sensors. The inertial measurement unit and wheel odometry provide motion and displacement data through mechanical sensing, substituting the power-intensive depth sensing approach while maintaining measurement precision through sensor fusion.
3Measurement precision
If marker based approaches are used for positional awareness, then measurement precision is improved, but adaptability deteriorates due to limited useful area
Solution Approach 1:
The patent extracts the positioning function from marker-based systems by using natural visual features detected by the monocular camera combined with auxiliary sensor data. This removes the requirement for artificial markers while maintaining location recognition accuracy, thereby expanding the operational area to any environment with detectable visual features.
Data Source
AI summary
The described positional awareness techniques employing sensory data gathering and analysis hardware with reference to specific example implementations implement improvements in the use of sensors, techniques and hardware design that can enable specific embodiments to provide positional awareness to machines with improved speed and accuracy. The sensory data are gathered from an operational camera and one or more auxiliary sensors.