Visual-Inertial Positional Awareness With Sensor-Module Offloading
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Solution Overview
Problem
Existing technologies face challenges in providing fast, accurate, and reliable positional awareness for autonomous robots and wearable devices, with methods like RFID/WiFi being expensive and inaccurate, depth sensors suffering from power drain and interference, and visual approaches being slow and prone to scale ambiguity.
Innovation Solution
Employing a visual-inertial sensor system that combines grayscale and colored cameras with an inertial measurement unit (IMU) to offload computational tasks from the main processor to a low-power sensor module, using techniques like time stamping, bias correction, and misalignment correction to enhance localization and tracking accuracy.
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, depth sensors, inertial sensors, RFID, WiFi) into a unified sensor suite that processes data through sensor fusion algorithms. This merging allows the system to leverage the broad coverage of RFID/WiFi while achieving high positioning accuracy through complementary visual and depth sensing, resolving the contradiction between coverage area and positioning accuracy.
2Measurement precision
If depth sensors are deployed for positional awareness, then measurement accuracy is improved, but power consumption increases and interference issues arise
Solution Approach 1:
The system implements periodic activation of depth sensors rather than continuous operation, using them in conjunction with visual sensors and inertial data. Depth sensors are activated at specific intervals or when visual-inertial tracking requires correction, reducing overall power consumption while maintaining positioning accuracy through strategic use of depth information.
3Ease of manufacture
If visual approaches are used for positional awareness, then cost is reduced, but processing speed decreases leading to failure in fast motion applications
Solution Approach 1:
The system segments the computational workload by using inertial sensors to provide high-speed motion estimation that operates independently of visual processing. Visual sensors then refine this estimation at lower speeds, allowing the system to maintain cost-effectiveness with standard cameras while achieving fast motion tracking through the parallel inertial measurement pathway.
4Ease of manufacture
If visual approaches are used for positional awareness, then cost is reduced, but scale ambiguity occurs leading to inaccurate positioning
Solution Approach 1:
The system introduces depth sensors and inertial sensors as intermediary measurement sources that provide scale information independent of visual cues. Depth sensors directly measure distances to provide absolute scale, while inertial sensors track motion magnitude, both serving as mediators that resolve the scale ambiguity inherent in pure visual approaches while maintaining 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.