Monocular-Auxiliary Sensor Fusion for Robot Positional Awareness
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Solution Overview
Problem
Current technologies for providing fast, accurate, and reliable positional awareness to robots are inadequate, especially in complex environments, as they fail to incorporate real-time sensory data and are often expensive, power-intensive, or limited by scale ambiguity and hardware requirements.
Innovation Solution
A monocular-auxiliary sensor system that uses visual data from a single camera, combined with inertial measurement units and wheel odometry data, to estimate positional information and adapt plans in real-time, leveraging low-end imaging sensors and offloading computation to reduce energy consumption and improve processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional SLAM techniques and visual approaches are used for positional awareness, then robots can identify their location and plan paths, but the systems are slow and fail in fast motion applications with scale ambiguity
Solution Approach 1:
The patent combines multiple sensing modalities (visual sensors, inertial sensors, wheel encoders) into a unified sensor fusion system. This merging allows the system to leverage the high-speed capability of inertial sensors for fast motion tracking while using visual sensors for accurate positioning, thereby resolving the contradiction between speed and precision in positional awareness.
Solution Approach 2:
The patent introduces an intermediary inertial measurement unit (IMU) that acts as a bridge between fast motion detection and accurate visual positioning. The IMU provides high-frequency motion data that compensates for the slow response of visual approaches, while visual data corrects the scale ambiguity of inertial data, enabling both speed and accuracy.
2Measurement precision
If depth sensor based approaches are used for positional awareness, then robots can achieve accurate positioning, but the systems suffer from high cost and power drain
Solution Approach 1:
The patent replaces expensive depth sensors with a combination of inexpensive components: a standard camera, low-cost inertial sensors, and wheel encoders. This substitution achieves comparable positioning accuracy while dramatically reducing hardware cost and power consumption, making the system suitable for resource-constrained mobile robots.
Solution Approach 2:
The patent substitutes active depth sensing (which requires significant power) with a passive visual system combined with mechanical odometry and inertial measurement. This replacement uses minimal power while maintaining positioning accuracy through sensor fusion and computational methods.
3Measurement precision
If marker based approaches are used for positional awareness, then robots can achieve accurate location identification, but the useful operating area is limited by required marker placement
Solution Approach 1:
The patent extracts the dependency on artificial markers by using natural visual features (edges, corners, textures) in the environment for positioning. This extraction of marker requirements allows the robot to operate in unprepared environments while maintaining accurate location identification through visual feature matching and SLAM techniques.
Solution Approach 2:
The patent creates a universal positioning system that works across diverse environments without requiring environment-specific modifications. The visual-inertial-odometry fusion system can operate in markerless environments, making it adaptable to various locations and scenarios, thereby enhancing operating area flexibility while maintaining positioning accuracy.
4Measurement precision
If RFID/WiFi approaches are used for positional awareness, then robots can achieve location identification, but the systems are expensive and of limited accuracy
Solution Approach 1:
The patent replaces expensive RFID/WiFi infrastructure with inexpensive consumer-grade components: standard cameras, low-cost inertial sensors, and wheel encoders. This substitution achieves superior positioning accuracy through sensor fusion while dramatically reducing both hardware cost and infrastructure requirements.
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 find new area to cover by a robot encountering an unexpected obstacle traversing an area in which the robot is performing an area coverage task. The sensory data are gathered from an operational camera and one or more auxiliary sensors.