Robot Positional Awareness Using Monocular Sensor Fusion
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Solution Overview
Problem
Current autonomous robot systems face challenges in providing fast, accurate, and reliable positional awareness, particularly in complex environments with changing conditions, as traditional SLAM techniques and task planning methods fail to incorporate real-time sensory data effectively.
Innovation Solution
The use of monocular-auxiliary sensors that combine visual data from a single camera with inertial measurement units and wheel odometry data to estimate positional information, allowing for real-time planning adjustments and obstacle avoidance, while offloading computational tasks to reduce energy consumption and enhance processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional SLAM techniques are used for positional awareness, then the system can provide location information, but the accuracy and reliability are insufficient in complex environments with changing conditions
Solution Approach 1:
The patent combines multiple sensing modalities (visual data from monocular camera, inertial data from IMU, and depth data from auxiliary sensors) into a unified sensor fusion framework. This multi-sensory integration allows the system to leverage complementary strengths of each sensor type, improving both accuracy and reliability of positional awareness in complex environments where single-sensor approaches fail.
Solution Approach 2:
The patent introduces an intermediary processing layer that fuses data from multiple sensors before feeding it to the SLAM algorithm. This intermediary fusion layer preprocesses and reconciles conflicting sensor measurements, providing more robust input to the positional estimation system and enhancing reliability under changing environmental conditions.
2Measurement precision
If high-end sensors and computational systems are used to improve positional awareness, then accuracy improves, but cost and energy consumption increase
Solution Approach 1:
The patent employs low-cost monocular cameras as the primary sensing modality instead of expensive depth sensors or multi-camera systems. While individual frames may have limited information content, the system processes sequences of frames to accumulate positional information, achieving acceptable accuracy with inexpensive hardware that consumes less power.
Solution Approach 2:
The system uses periodic processing of visual frames at optimized intervals rather than continuous high-rate processing. By processing keyframes selectively and using predictive models between frames, the system reduces computational energy consumption while maintaining adequate positional awareness accuracy for autonomous navigation.
3Adaptability or versatility
If real-time sensory data is incorporated into task planning, then the system can adapt to changing conditions, but computational complexity increases
Solution Approach 1:
The patent divides the computational task into separate modules: sensor data acquisition, sensor fusion processing, SLAM positional estimation, and task planning. Each module operates independently with well-defined interfaces, allowing real-time sensory data to be incorporated into planning without overwhelming the system. The segmentation enables parallel processing and optimizes computational resource allocation.
Solution Approach 2:
The system performs preliminary sensor fusion and positional estimation before task planning executes. By pre-processing sensory data and maintaining an updated environmental model in advance, the system reduces the computational burden during real-time planning decisions, enabling adaptation to changing conditions without excessive complexity.
4Ease of manufacture
If visual approaches are used for positional awareness, then cost is reduced, but processing speed becomes insufficient for fast motion applications
Solution Approach 1:
The patent merges monocular visual processing with inertial measurement unit (IMU) data to create a hybrid positioning system. The IMU provides high-frequency motion cues that compensate for the slower processing speed of visual approaches, enabling the low-cost system to handle fast motion applications effectively 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 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.