VSLAM Point Cloud Integration for Reverse Parking Stability
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Solution Overview
Problem
Conventional Visual SLAM (VSLAM) processing often experiences a shortage of position information for surrounding objects, leading to unstable detection of both self-position and surrounding objects, particularly in scenarios like reverse parking where the availability of position information is limited.
Innovation Solution
The implementation of an information processing device equipped with multiple image capturing units that capture images from different directions, integrating point cloud information from front and rear VSLAM processing to generate stable and comprehensive environmental maps, using alignment and integration processing to combine point cloud data and correct positional information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If VSLAM processing is performed using a single image capturing unit, then the device complexity is reduced, but the position information of surrounding objects becomes insufficient leading to unstable detection
Solution Approach 1:
The patent combines point cloud information from multiple image capturing units (front camera and rear camera) into a unified environmental map. The map generation unit integrates point cloud data from both cameras, merging front view point clouds and rear view point clouds to create comprehensive environmental information that improves detection stability while managing device complexity through software integration.
2Loss of information
If multiple image capturing units are used to capture images from different directions, then the position information of surrounding objects is improved, but the device complexity increases
Solution Approach 1:
The patent adds a temporal dimension to the spatial information by integrating point cloud data from different time points and different viewing directions. The environmental map combines front view data and rear view data obtained at different times, creating a comprehensive three-dimensional representation that compensates for information loss in any single view.
3Measurement precision
If point cloud information from front and rear VSLAM processing is integrated, then the environmental map accuracy is improved, but the processing time increases
Solution Approach 1:
The system performs VSLAM processing independently on front and rear image data simultaneously, generating point cloud information in parallel before integration. This preliminary processing of separate data streams allows the subsequent map generation to simply combine pre-processed point clouds, reducing the overall processing time compared to sequential processing.
Data Source
AI summary
An information processing device includes a VSLAM processing unit as an acquisition unit, a difference calculation unit and an offset processing unit as alignment processing units, and an integration unit as an integration processing unit. The VSLAM processing unit acquires first point cloud information based on first image data obtained from a first image capturing unit provided at a first position of a moving body, and acquires second point cloud information based on second image data obtained from a second image capturing unit provided at a second position different from the first position of the moving body. The difference calculation unit and offset processing unit perform alignment processing on the first and second point cloud information. The integration unit generates integrated point cloud information by using the first point cloud information and the second point cloud information on both of which the alignment processing has been performed.