Monocular Sensor Fusion for Low-Power Autonomous Platform Guidance
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Solution Overview
Problem
Current technologies for providing fast, accurate, and reliable positional awareness to autonomous robots and self-guiding mobile platforms are limited by the inefficiencies of RFID/WiFi, depth sensors, and visual approaches, which are costly, power-intensive, and suffer from accuracy and scale ambiguity issues.
Innovation Solution
A monocular-auxiliary sensor system that combines visual data from a single camera with inertial measurement units (IMU) and wheel odometry data to estimate positional information, using low-end imaging sensors and offloading computational tasks to reduce power consumption and enhance energy efficiency.
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 combines data from multiple auxiliary sensors (inertial measurement units, wheel odometry, barometers, GPS) with monocular visual data to achieve accurate positional awareness without relying on power-intensive depth sensors. This fusion approach merges complementary information sources to compensate for the limitations of each individual sensor while reducing overall energy consumption.
Solution Approach 2:
The system uses a single monocular camera to perform multiple functions: visual odometry for position estimation, feature extraction for navigation, and scale estimation when combined with auxiliary sensors. This multi-functional use of the camera reduces the need for specialized expensive sensors while maintaining measurement precision.
2Measurement precision
If depth sensors are used for positional awareness, then measurement precision is improved, but device cost increases
Solution Approach 1:
The patent combines data from multiple auxiliary sensors (inertial measurement units, wheel odometry, barometers, GPS) with monocular visual data to achieve accurate positional awareness without relying on expensive depth sensors. This fusion approach merges complementary information sources to compensate for the limitations of each individual sensor while reducing overall system cost.
Solution Approach 2:
The system replaces expensive depth sensors with a combination of inexpensive components: a standard monocular camera and low-cost auxiliary sensors (IMU, wheel encoders, barometer, GPS). While individual components have shorter lifetimes or lower precision alone, their combination provides sustained accurate performance at fraction of the cost of depth sensors.
3Ease of manufacture
If visual approaches are used for positional awareness, then device cost is reduced, but measurement precision deteriorates due to scale ambiguity
Solution Approach 1:
The patent introduces auxiliary sensors as intermediary components that mediate between the monocular camera and the final position estimate. The inertial measurement unit provides acceleration data to estimate distance traveled, the barometer provides altitude information, and wheel odometry provides motion data. These intermediaries resolve the scale ambiguity inherent in monocular visual data while keeping the system low-cost.
Solution Approach 2:
The system changes the parameters used for position estimation by combining visual features with physical measurements from auxiliary sensors. Instead of relying solely on visual scale estimation (which is ambiguous), the system incorporates inertial acceleration, barometric pressure changes, and wheel rotation data to compute accurate position, velocity, and orientation parameters.
4Measurement precision
If high-end imaging sensors are used for positional awareness, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system replaces high-end imaging sensors with a standard monocular camera combined with auxiliary sensors. The auxiliary sensors (IMU, wheel odometry, barometer) provide the additional measurement precision that would otherwise require expensive camera hardware, thereby reducing power consumption while maintaining accuracy.
Solution Approach 2:
The patent merges data from the monocular camera with auxiliary sensors to achieve the measurement precision that would otherwise require high-end imaging sensors alone. This combination allows the use of lower-power, standard camera hardware while achieving high-precision positional awareness through sensor fusion.
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.