Semantic Sensor Weighting for Robust Robotic Navigation
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Solution Overview
Problem
Autonomous robotic devices face navigation challenges due to temporal and spatial variations in their environments, which can affect sensor reliability and accuracy, particularly in indoor settings where lighting conditions and object distribution change, leading to unreliable sensor data.
Innovation Solution
The robotic device employs semantic information extraction to identify adverse events affecting sensor performance, adjusts weight factors for sensor measurements based on recorded patterns of low performance, and uses this information to improve localization and mapping processes through SLAM techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the robotic device uses sensor measurements for navigation in varying environmental conditions, then the device can operate autonomously, but the sensor reliability and measurement accuracy deteriorate due to temporal and spatial variations in the environment
Solution Approach 1:
The patent implements dynamic adjustment of sensor weight factors based on environmental conditions. The system continuously monitors sensor performance and adapts the weighting of different sensor measurements in real-time, transitioning from static sensor fusion to dynamic adaptation. This allows the robotic device to maintain autonomous navigation capability while compensating for varying sensor reliability in different temporal and spatial conditions.
Solution Approach 2:
The system changes the parameters of sensor data processing by adjusting weight factors assigned to different sensors based on environmental conditions. When certain sensors exhibit degraded performance due to lighting changes, obstruction, or other environmental factors, the system modifies the parameter weights to emphasize more reliable sensors, thereby maintaining overall navigation reliability while preserving autonomous operation.
2Device complexity
If the robotic device applies uniform weight factors to all sensor measurements, then the system complexity is reduced, but the localization and mapping accuracy deteriorates under varying environmental conditions
Solution Approach 1:
The patent applies local quality by assigning different weight factors to different sensors based on their performance in specific environmental conditions. Instead of treating all sensor measurements uniformly, the system evaluates each sensor's reliability in the current context and applies localized weighting adjustments. This enables improved localization accuracy by emphasizing reliable sensors while downweighting problematic ones, without requiring complete system redesign.
3Quantity of substance
If the robotic device uses all sensor data without filtering, then the data utilization is maximized, but the navigation accuracy deteriorates due to adverse events affecting certain sensors
Solution Approach 1:
The system implements partial action by selectively utilizing sensor data based on environmental conditions and sensor performance. Rather than using all sensor data unconditionally, the system applies weight factors that can reduce or eliminate the contribution of sensors experiencing adverse events. This selective utilization maintains high navigation accuracy by filtering out unreliable data while preserving useful information from reliable sensors.
Data Source
AI summary
Various embodiments include methods for improving navigation by a processor of a robotic device. Such embodiments may include initiating a start of a predetermined time period associated with semantic information extraction, and determining whether an adverse event related to one or more sensors of the robotic device is detected. Such embodiments may also include identifying a current time slot of the predetermined time period, identifying a current estimated position and orientation of the robotic device, and recording updates to semantic information stored for the one or more sensor based on the identified current time slot and the current estimated position and orientation of the robotic device in response to determining that an adverse event related to one or more sensors of the robotic device is detected.