Visual-Inertial Sensor Fusion for Low-Power Positional Awareness
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Solution Overview
Problem
Current technologies for providing fast, accurate, and reliable positional awareness for robots and wearable devices face challenges such as high costs, power consumption issues, and limitations in recognizing locations and obstructions quickly, with existing methods like RFID/WiFi, depth sensors, and visual approaches being expensive, power-intensive, or slow.
Innovation Solution
The use of visual-inertial sensory data gathering and analysis hardware, which combines stereo imaging sensors and multi-axis inertial measurement units to estimate changes in the environment, correct for drift issues, and reduce computational and energy burdens by offloading processing to a low-power sensor module, employing low-cost grayscale and colored sensors for localization and recognition tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If RFID/WiFi approaches are used for positional awareness, then device cost and power consumption are reduced, but measurement precision and reliability deteriorate
Solution Approach 1:
The patent combines multiple sensing modalities (visual sensors, depth sensors, inertial sensors, RFID, WiFi) into a unified sensor fusion system. This merging allows the system to leverage the low power consumption of RFID/WiFi while compensating for their precision limitations through complementary data from visual and depth sensors, achieving both energy efficiency and accurate positional awareness.
Solution Approach 2:
The system employs a composite sensing architecture that integrates different types of sensors with complementary characteristics. By combining the low-power wireless positioning (RFID/WiFi) with high-precision visual and depth sensors, the system creates a hybrid solution that achieves reliable positional accuracy without the power consumption of using only high-precision sensors continuously.
2Measurement precision
If depth sensor based approaches are used, then measurement precision is improved, but use of energy increases and interference issues arise
Solution Approach 1:
The system implements periodic or event-triggered depth sensing rather than continuous operation. Depth sensors are activated only when needed for specific tasks or when visual sensors detect ambiguous situations, reducing overall power consumption while maintaining positional accuracy when required.
Solution Approach 2:
The system dynamically adjusts sensor activation and data fusion strategies based on operational context, motion detection, and environmental conditions. This dynamic approach allows the system to use depth sensors intensively when high precision is needed while relying on lower-power sensors during stable periods, optimizing the trade-off between accuracy and power consumption.
3Ease of manufacture
If visual approaches are used, then device cost is reduced, but speed of recognition deteriorates leading to failure in fast motion applications
Solution Approach 1:
The system performs preliminary processing of visual data through feature extraction and keyframe selection before full recognition processing. By pre-identifying salient features and potential interest points, the system reduces the computational burden during fast motion, enabling quicker recognition responses while maintaining low device cost through efficient algorithms.
Solution Approach 2:
The visual processing pipeline is segmented into multiple stages: fast feature detection, intermediate matching, and detailed recognition. This segmentation allows the system to quickly filter and process visual data in real-time for cost-effective hardware, while maintaining recognition speed by processing only relevant portions of visual information at each stage.
4Reliability
If visual-inertial sensor fusion is implemented, then reliability and accuracy are improved, but device complexity increases
Solution Approach 1:
The patent implements a universal sensor fusion framework that can accommodate multiple sensor types (visual, inertial, depth, RFID, WiFi) through a common processing architecture. This multi-functional approach allows the system to achieve high reliability by fusing data from available sensors while using a standardized processing pipeline that reduces overall system complexity compared to separate dedicated systems for each sensor type.
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.