Visual-Inertial Sensor Fusion for Accurate Positional Awareness
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Solution Overview
Problem
Current technologies for providing fast, accurate, and reliable positional awareness to robots and wearable devices, such as VR/AR headsets, face challenges including high computational costs, energy inefficiency, and high costs due to reliance on expensive sensors like stereo RGB systems and active sensing methods, which limit their widespread adoption.
Innovation Solution
The implementation of a visual-inertial sensor system that offloads computational tasks from the main processor to a low-power sensor module, using a combination of grayscale and colored cameras with inertial measurement units (IMUs) to process image and inertial data, reducing costs and energy consumption while maintaining performance, and employing techniques like image undistortion, feature detection, and sensor fusion for accurate localization and tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If depth sensor based approaches 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 active depth sensing mechanisms (which consume significant power) with a passive visual-inertial system. The visual system uses grayscale and color cameras to capture images, while the inertial system uses IMUs to measure motion. By fusing these passive sensing modalities, the system achieves depth and position information without the high energy consumption of active depth sensors.
Solution Approach 2:
The patent changes the sensing parameters from active depth measurement (high energy) to passive visual feature detection combined with inertial motion capture. The system processes image data to extract visual features and fuses them with inertial measurement data, fundamentally changing how positional information is obtained to reduce energy consumption while maintaining accuracy.
2Ease of manufacture
If visual approaches are used for positional awareness, then device cost is reduced, but measurement precision deteriorates due to slow processing and scale ambiguity
Solution Approach 1:
The patent merges visual sensing (cameras) with inertial sensing (IMUs) into a unified visual-inertial system. The visual component provides cost-effective positioning but suffers from scale ambiguity and slow processing. The inertial component provides fast motion capture and scale information. By fusing these complementary systems, the patent achieves both low cost and high precision positional awareness.
Solution Approach 2:
The inertial measurement unit acts as an intermediary that bridges the gap between visual feature matching and accurate 3D positioning. The IMU provides motion data that helps resolve scale ambiguity in visual approaches and accelerates processing by providing direct motion measurements, thereby improving precision while maintaining the cost benefits of visual sensing.
3Measurement precision
If marker based approaches are used for positional awareness, then measurement precision is improved, but adaptability deteriorates due to limited operational area
Solution Approach 1:
The patent replaces marker-based positioning (which requires physical markers in the environment) with markerless visual-inertial positioning. The system uses natural visual features from grayscale and color images combined with inertial motion data to achieve accurate positioning without requiring any special markers or pre-configured environmental elements, thereby expanding the useful operating area to unlimited spaces.
4Adaptability or versatility
If RFID/WiFi approaches are used for positional awareness, then adaptability is improved, but measurement precision deteriorates and device cost increases
Solution Approach 1:
The patent replaces RFID/WiFi-based positioning (which uses wireless signal triangulation) with visual-inertial positioning. The visual-inertial system achieves superior accuracy by directly measuring visual features and inertial motion, while maintaining adaptability through markerless operation. This substitution eliminates the precision limitations of signal-based methods while keeping the system flexible and wireless.
Data Source
AI summary
The described positional awareness techniques employing visual-inertial 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.