Robot SLAM Dynamic Obstacle Removal for Accurate Localization
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Solution Overview
Problem
Existing robot localization systems struggle in dynamic environments, particularly in large spaces like factories, due to difficulties in distinguishing dynamic from static objects, leading to inaccurate mapping and localization, and the need for manual intervention to remove dynamic obstacles, which is time-consuming and prone to errors.
Innovation Solution
An apparatus and method that utilize a simultaneous localization and mapping (SLAM) technique to detect and remove dynamic obstacles while updating maps in real-time, allowing for robust localization and editing 3D SLAM data through user manipulation, optimizing pose graphs and generating 2D grid maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intervention is used to remove dynamic obstacles from maps, then dynamic obstacles can be removed, but it is time-consuming and prone to errors
Solution Approach 1:
The system performs automatic dynamic obstacle detection and removal through the processor analyzing sensor data and tracking information, eliminating the need for manual user intervention. The processor independently identifies dynamic obstacles based on tracking information and removes them from the map data structure, making the system self-sufficient in maintaining map accuracy.
2Measurement precision
If map building and localization are performed separately to increase localization accuracy, then localization accuracy is improved, but the system cannot adapt to dynamic environments where objects frequently change
Solution Approach 1:
The system implements dynamic obstacle detection and removal capabilities that allow the map to adapt to changing environments. The processor continuously tracks objects using tracking information from sensor data and dynamically removes detected dynamic obstacles from the map, enabling the system to maintain accuracy in environments where objects frequently change position or appearance.
3Ease of operation
If existing map editors are used to modify 2D grid maps, then map modifications can be made, but 3D SLAM data such as key frames and edges cannot be directly edited
Solution Approach 1:
The processor performs multiple functions including dynamic obstacle detection, tracking information analysis, and automatic removal of dynamic obstacles from map data. This multi-functional approach combines obstacle detection and map maintenance capabilities into a single system that operates on the existing SLAM data structure without requiring separate specialized tools.
4Stability of the object's composition
If dynamic obstacles are not removed from the map, then the map can be maintained as-is, but the robot may unnecessarily plan detour paths or have difficulty localizing
Solution Approach 1:
The processor extracts and removes dynamic obstacle information from the map data structure by analyzing tracking information and identifying objects that do not match the static map. This extraction process separates dynamic elements from the static map, allowing the robot to navigate efficiently using the cleaned map data while maintaining the integrity of the underlying map structure.
Data Source
AI summary
The apparatus for detecting and removing a dynamic obstacle of a robot and the operating method thereof according to a predetermined exemplary embodiment detect and remove the dynamic obstacle while simultaneously performing the mapping and the localizing using the simultaneous localization and mapping (SLAM) technique to efficiently detect and remove a dynamic obstacle even in a situation in which a dynamic change of surrounding environment is severe and an environment to be localized is large.