Robot Object Detection Using Sensor Fusion Redundancy
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Solution Overview
Problem
Existing sensor systems in robots, particularly autonomous vehicles, suffer from unreliable object detection due to non-detection or erroneous detection, which can lead to inappropriate control maneuvers.
Innovation Solution
A method involving sensor fusion of multiple detection devices, generating redundancy information to enhance object detection reliability by distinguishing between redundant and non-redundant detections, allowing for differentiated control maneuvers based on detection certainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple sensors are used to detect objects in the environment, then the coverage and detection capability are improved, but the reliability of object detection deteriorates due to non-detection or erroneous detection by individual sensors
Solution Approach 1:
The patent combines detection data from multiple sensors (camera, radar, lidar) using sensor fusion to create a unified object detection result. By merging the strengths of different sensor types, the system achieves more reliable detection than individual sensors alone, resolving the contradiction between enhanced detection capability and maintained reliability.
Solution Approach 2:
The system generates redundancy information that provides feedback on the quality and reliability of object detections. This feedback mechanism allows the control system to adjust its responses based on detection certainty, improving overall system reliability while maintaining the benefits of multiple sensors.
2Reliability
If sensor fusion is performed to improve object detection reliability, then the detection accuracy is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the object detection process into distinct stages: initial object detection from sensor data, generation of redundancy information, and differentiated control based on detection certainty. This segmentation allows complex sensor fusion to be managed in manageable steps, reducing processing complexity while maintaining reliability.
Solution Approach 2:
The system performs partial sensor fusion by generating redundancy information only for detected objects rather than processing all sensor data completely. This selective approach maintains detection reliability while reducing unnecessary computational complexity.
3Measurement precision
If redundancy information is generated for all detected objects, then the ability to distinguish true objects from false detections is improved, but the data processing load and system complexity increase
Solution Approach 1:
The system generates redundancy information selectively for detected objects rather than processing all possible detections equally. This partial action approach maintains high detection accuracy for critical objects while reducing unnecessary data processing overhead.
Solution Approach 2:
The patent applies different levels of redundancy analysis based on local conditions - generating redundancy information where needed to improve detection precision while avoiding unnecessary processing in areas with high confidence detections, thus balancing accuracy with processing efficiency.
Data Source
AI summary
A method and device are disclosed for improved object detection in an area surrounding a robot. In the method, first and second sensing data are obtained, which can be assigned to a first or second sensing means of the robot, respectively, and which contain at least one portion of the area surrounding the robot. An objection detection of an object in the area surrounding the robot is carried out using a fusion of at least the first and the second sensing data. An item of redundancy information is generated, which is assigned to the object detection and at least indicates whether the detected object has been detected using only the first or only the second sensing data or whether the detected object or at least one or more sections of same has been detected redundantly using both the first and the second sensing data.