Robot Relocalization Mapping With Monocular-Auxiliary Sensors
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Solution Overview
Problem
Conventional approaches 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 struggle with relocalization when robots lose track of their position, leading to inefficiencies and inaccuracies in task planning and execution.
Innovation Solution
The implementation of a monocular-auxiliary sensor system that utilizes visual data from a camera, combined with inertial measurement units and wheel odometry data, to enable robots to relocalize themselves by creating a local submap and merging it into a global map, allowing for real-time planning adjustments and energy-efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional SLAM techniques are used for robot localization, then positional awareness can be provided, but the accuracy and reliability deteriorate when the robot loses track of its position in complex environments
Solution Approach 1:
The system performs preliminary mapping by creating a global map of the environment before the robot needs to relocalize. This pre-established map contains feature information that can be quickly compared against current sensor data when the robot loses track of its position, enabling fast relocalization without needing to rebuild the environment model from scratch
Solution Approach 2:
The patent introduces an intermediary relocalization module that acts as a bridge between the robot's current visual/inertial data and the global map. This intermediary component processes sensor data to identify matching features and compute the robot's position, resolving the contradiction by providing a specialized pathway for position recovery that doesn't rely on the continuous tracking mechanisms that fail
2Speed
If visual processing approaches are used for fast motion applications, then real-time data can be captured, but the processing speed becomes insufficient leading to tracking failure
Solution Approach 1:
The system segments the processing task by dividing it into multiple parallel processing streams: one for visual feature extraction, another for inertial sensor processing, and a third for odometry data. This segmentation allows each stream to process data independently and concurrently, increasing overall processing speed while maintaining tracking reliability through sensor fusion
Solution Approach 2:
The patent implements dynamic processing where the system adapts its processing strategy based on current conditions. When the robot is moving fast, the system prioritizes inertial and odometry data which can process at higher frequencies, while reducing reliance on visual processing. This dynamic adjustment maintains tracking reliability across varying speed conditions
3Reliability
If comprehensive sensor fusion is implemented for accurate localization, then positioning reliability improves, but computational burden and energy consumption increase
Solution Approach 1:
The system implements partial sensor fusion by selectively combining sensor data based on current operational needs and conditions. Rather than continuously fusing all available sensor data, the system activates specific fusion algorithms only when needed (e.g., during relocalization events or when confidence in current position drops), reducing computational energy consumption while maintaining positioning accuracy when it matters most
Solution Approach 2:
The patent changes processing parameters dynamically based on operational context. During normal operation, the system uses lower-computation modes with relaxed accuracy thresholds. When relocalization is detected or required, the system switches to higher-computation modes with stricter accuracy requirements. This parameter changing strategy maintains positioning accuracy during critical moments while reducing energy consumption during routine operation
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 performing an area coverage task of an unexplored area. The sensory data are gathered from an operational camera and one or more auxiliary sensors.