Occlusion Detection for Mobile Robot SLAM Navigation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Mobile robots face challenges in navigating environments with occlusions, as existing technologies struggle to accurately detect and adapt to obstructions in their field of view, affecting their ability to perform Simultaneous Localization and Mapping (SLAM) tasks effectively.
Innovation Solution
A mobile robot system equipped with a machine vision system and a processor that uses occlusion detection data to identify and notify about obstructions in its field of view, allowing the SLAM application to adjust and maintain accurate mapping and localization, featuring a tilted camera with a narrow field of view and occlusion detection mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a mobile robot uses a machine vision system to perform SLAM tasks, then the robot can navigate and build maps of its environment, but occlusions in the field of view reduce the reliability of detection and mapping
Solution Approach 1:
The system performs preliminary occlusion detection by analyzing captured images to identify occlusions before they significantly impact SLAM operations. The processor detects occlusions in the field of view and generates notifications, allowing the robot to take preventive actions or adjust its navigation plan before mapping accuracy deteriorates.
Solution Approach 2:
The system implements a feedback mechanism where the processor continuously monitors the field of view for occlusions and provides real-time notifications to the control system. This feedback loop enables the robot to adapt its navigation behavior based on detected occlusions, maintaining reliable SLAM performance despite environmental obstructions.
2Reliability
If the robot maintains continuous occlusion detection, then navigation reliability improves, but computational resources and processing time increase
Solution Approach 1:
The system applies partial occlusion detection by focusing analysis on specific regions of the field of view that are most critical for SLAM operations. Rather than processing every pixel uniformly, the processor identifies and analyzes portions of images that correspond to important navigation and mapping areas, reducing computational complexity while maintaining navigation reliability.
Solution Approach 2:
The occlusion detection process is segmented into distinct processing stages: image capture, occlusion identification, and notification generation. The processor divides the field of view into relevant regions and processes them separately, allowing efficient resource allocation and reducing overall computational complexity while maintaining detection reliability.
Data Source
AI summary
The present invention provides a mobile robot configured to navigate an operating environment, that includes a machine vision system comprising a camera that captures images of the operating environment using a machine vision system; detects the presence of an occlusion obstructing a portion of the field of view of a camera based on the captured images, and generate a notification when an occlusion obstructing the portion of the field of view of the camera is detected, and maintain occlusion detection data describing occluded and unobstructed portions of images being used by the SLAM application.