Monocular Visual-Inertial Guidance for Low-Power Robot Positioning
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Solution Overview
Problem
Current technologies for providing fast, accurate, and reliable positional awareness to autonomous robots and mobile platforms are limited by the high cost, power consumption, and interference issues of existing methods such as RFID/WiFi, depth sensors, and marker-based approaches, which fail to meet the standards for widespread adoption.
Innovation Solution
A monocular-auxiliary sensor system that utilizes a single operational camera with inertial measurement units (IMU) and wheel odometry units to estimate positional information, offloading computational tasks to low-power modules and employing image processing techniques like stereo matching, feature extraction, and sensor fusion to enhance energy efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If depth sensors are used for positional awareness, then measurement precision is improved, but use of energy increases and device cost increases
Solution Approach 1:
The patent replaces depth sensors (mechanical/optical sensing system) with a monocular camera combined with inertial measurement units and visual processing algorithms. This substitution maintains positional awareness accuracy while significantly reducing power consumption by using computationally processed visual data instead of active depth sensing hardware.
Solution Approach 2:
The patent introduces visual features and inertial measurement units as intermediary elements between the monocular camera and the final positional awareness output. These intermediaries enable accurate position estimation through sensor fusion and visual processing without requiring power-intensive depth sensors.
2Ease of manufacture
If RFID/WiFi approaches are used for positional awareness, then device cost is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent replaces RFID/WiFi radio-based positioning with a visual-inertial system using a monocular camera and IMU. This substitution achieves superior measurement precision through visual feature tracking and inertial fusion while maintaining cost-effectiveness by using standard camera and sensor components rather than specialized radio infrastructure.
Solution Approach 2:
The patent changes the measurement parameters from radio signal characteristics (RFID/WiFi) to visual feature parameters (image coordinates, optical flow) and inertial parameters (acceleration, orientation). This parameter transformation enables high-precision positioning through visual and motion data while avoiding the need for expensive radio infrastructure.
3Measurement precision
If marker-based approaches are used for positional awareness, then measurement precision is improved, but device complexity increases and adaptability deteriorates
Solution Approach 1:
The patent extracts the requirement for artificial markers from the system by using natural visual features (corners, edges, textured patterns) in the environment. This extraction eliminates the need to place markers in the workspace, thereby maintaining high measurement precision through visual feature tracking while fully restoring adaptability and operational flexibility.
Solution Approach 2:
The patent creates a universal positioning system that works across diverse environments without requiring environment-specific modifications. The monocular visual-inertial system can operate in any space with sufficient visual features, making it adaptable to various applications (robotics, AR, VR, autonomous vehicles) without needing marker placement or environment preparation.
4Ease of manufacture
If visual approaches are used for positional awareness, then device cost is reduced, but speed deteriorates leading to failure in fast motion applications
Solution Approach 1:
The patent merges monocular visual processing with inertial measurement unit data to create a hybrid positioning system. The IMU provides high-speed motion information that compensates for the slower visual processing, enabling the system to handle fast motion applications while maintaining cost-effectiveness through the use of a single camera rather than expensive stereo or depth sensor systems.
Solution Approach 2:
The patent performs preliminary action by using the inertial measurement unit to predict motion between visual frames. This prediction allows the visual processing to focus on correcting and refining the position estimate rather than computing everything from scratch, thereby increasing processing speed and enabling fast motion handling while keeping device costs low.
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.