Visual-Inertial Localization for Low-Power Positional Awareness
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Solution Overview
Problem
Existing technologies for providing fast, accurate, and reliable positional awareness to robots and wearable devices are limited by high costs, computational burdens, power consumption, and interference issues, with conventional approaches failing to meet widespread adoption standards.
Innovation Solution
Implementing a visual-inertial sensor system that offloads computational tasks from the main processor to a low-power sensor module, using low-cost grayscale and colored cameras, and inertial sensors to achieve efficient localization and recognition, with data processing optimized for energy efficiency and reduced cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If RFID/WiFi approaches are used for positional awareness, then coverage area is extended, but cost increases and accuracy is limited
Solution Approach 1:
The patent combines multiple sensing modalities (visual sensors, inertial sensors, depth sensors) into an integrated sensor system that processes data from multiple sources simultaneously. This fusion approach achieves both wide coverage and high accuracy by compensating for the limitations of individual sensors through their complementary strengths.
2Measurement precision
If depth sensor based approaches are used, then positional accuracy is improved, but cost increases and power consumption increases
Solution Approach 1:
The system employs periodic sensing and processing cycles where depth sensors are activated only when needed for specific tasks, rather than continuously. The inertial sensors provide continuous low-power tracking, while depth sensing is supplemented periodically or selectively, reducing overall power consumption while maintaining accuracy when required.
3Measurement precision
If marker based approaches are used, then localization accuracy is improved, but operational area is limited
Solution Approach 1:
The system uses visual features and natural environment markers that are already present in the scene, eliminating the need for artificial markers to be placed in the environment. The sensor system automatically detects and utilizes available visual cues for localization, enabling operation in any environment without requiring pre-prepared marker infrastructure.
4Ease of manufacture
If visual approaches are used for fast motion applications, then cost is reduced, but speed performance deteriorates
Solution Approach 1:
The system uses inertial sensors to predict and pre-calculate positional changes during fast motion, before visual processing is complete. This preliminary inertial-based estimation provides immediate speed feedback, while visual processing continues in parallel to refine the position data, maintaining both speed performance and cost-effectiveness.
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.