Fusion Map Construction for Robot Localization in Dark and Dynamic Scenes
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Solution Overview
Problem
Robots struggle to operate and localize effectively in complex scenarios such as dynamic and dark environments due to reliance on laser or visual SLAM, limiting their operational range.
Innovation Solution
A fusion map construction method that integrates laser radar and image collection data to interpolate image frames, select key frames, and construct a 3D map, combining laser and visual data for enhanced localization and operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If robots use laser SLAM for operation and localization, then they can operate in dark scenarios, but they cannot operate normally in dynamic scenarios
Solution Approach 1:
The patent combines laser SLAM and visual SLAM into a unified fusion mapping system. The laser mapping module generates laser maps for dark environments while the visual mapping module generates visual maps for dynamic environments. These two mapping systems are merged through coordinate transformation and fusion algorithms to create a comprehensive fusion map that enables robots to operate in both dark and dynamic scenarios simultaneously.
2Adaptability or versatility
If robots use visual SLAM for operation and localization, then they can operate in dynamic scenarios, but they cannot operate normally in dark scenarios
Solution Approach 1:
The patent combines laser SLAM and visual SLAM into a unified fusion mapping system. The laser mapping module generates laser maps for dark environments while the visual mapping module generates visual maps for dynamic environments. These two mapping systems are merged through coordinate transformation and fusion algorithms to create a comprehensive fusion map that enables robots to operate in both dark and dynamic scenarios simultaneously.
3Device complexity
If robots rely on single-mode SLAM (laser or visual), then the system complexity is low, but the operational range is limited
Solution Approach 1:
The patent combines laser SLAM and visual SLAM into a unified fusion mapping system. The laser mapping module generates laser maps for dark environments while the visual mapping module generates visual maps for dynamic environments. These two mapping systems are merged through coordinate transformation and fusion algorithms to create a comprehensive fusion map that enables robots to operate in both dark and dynamic scenarios simultaneously.
Solution Approach 2:
The fusion mapping system serves multiple functions: it can operate in dark scenarios using laser data, in dynamic scenarios using visual data, and in normal scenarios using fused data. The system universally handles different environmental conditions through a unified architecture that selects and combines appropriate sensing and mapping methods based on current conditions.
Data Source
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AI summary
A fused map construction method and apparatus, and a robot and a storage medium. The fused map construction method comprises: acquiring an image frame set collected by an image collection device and a laser pose data set obtained by means of lidar mapping, and on the basis of the laser pose data set, performing an interpolation operation on each image frame in the image frame set, so as to obtain each pose corresponding to each image frame (S100); acquiring a feature matching relationship between adjacent image frames, selecting a key frame set from the image frame set on the basis of the pose corresponding to each image frame, and the feature matching relationship, acquiring an initial map point set corresponding to the key frame set, and constructing a correspondence between each key frame in the key frame set and each initial map point in the initial map point set, so as to obtain a correspondence set (S102); sequentially selecting from the key frame set the current key frame and a related key frame set corresponding to the current key frame, and updating the initial map point set and the correspondence set on the basis of a feature matching relationship between the current key frame and the related key frame set, so as to obtain an updated map point set and an updated correspondence set (S104); and converting the updated map point set into a three-dimensional map point set, and constructing a target fused map on the basis of the three-dimensional map point set, the updated correspondence set, and the pose of each key frame in the correspondence set (S106).