3D Map Generation Using LiDAR SLAM and Structure-From-Motion
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Solution Overview
Problem
Existing methods for generating 3D maps for visual localization in large-scale indoor spaces face challenges due to changes in viewpoint and congestion, making it difficult to apply Structure-From-Motion (SFM) effectively, and current solutions like RGB-D cameras and laser scanners are not suitable for large-scale environments.
Innovation Solution
A method and system utilizing a new pipeline that combines Lidar Simultaneous Localization and Mapping (SLAM) with Structure-From-Motion (SFM) techniques, collecting spatial and image data over a predetermined period to generate a precise 3D feature map, using Lidar sensors and camera sensors to estimate movement trajectories and extract feature points, and optimize pose information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Structure-From-Motion (SFM) is applied for visual localization, then location determination precision is improved, but it becomes difficult to apply in large-scale indoor spaces due to viewpoint changes and congestion
Solution Approach 1:
The patent segments the large-scale indoor space mapping task into two distinct phases: first using LiDAR SLAM to generate an initial 3D map and estimate device trajectory, then using SFM to refine the map and trajectory. This segmentation allows each method to operate in its optimal environment - LiDAR SLAM for robust initial mapping in large spaces, and SFM for high-precision refinement where visual features are available.
Solution Approach 2:
The patent introduces LiDAR SLAM as an intermediary step between raw sensor data and final visual localization. The LiDAR-based 3D map and trajectory estimation serve as intermediate results that provide a stable geometric framework, which then enables SFM to focus on refining positional accuracy using visual features without being overwhelmed by the challenges of large-scale indoor environments.
2Measurement precision
If RGB-D camera or laser scanner is used for indoor mapping, then mapping precision is improved, but the measurement distance is limited and not suitable for large-scale spaces
Solution Approach 1:
The patent merges LiDAR-based mapping with vision-based refinement into a unified pipeline. The LiDAR component provides accurate geometric structure and trajectory estimation over large distances, while the vision component adds detailed visual features and refines positioning. This combination allows the system to overcome the distance limitations of individual sensors.
Solution Approach 2:
The patent creates a multi-functional mapping system that can operate effectively across different spatial scales. The LiDAR SLAM component handles the large-scale environmental understanding and trajectory estimation, while the SFM component provides precise visual localization. This universal system can adapt to both large-scale navigation and precise positioning requirements.
3Measurement precision
If data is collected with time intervals for a predetermined period, then map precision for large-scale space is improved, but data collection time and processing complexity increase
Solution Approach 1:
The patent performs preliminary action by using LiDAR SLAM to generate an initial 3D map and trajectory estimation before applying SFM refinement. This preliminary LiDAR-based mapping provides a stable geometric framework that guides subsequent visual processing, allowing the system to focus computational resources on refining specific areas rather than processing all visual data from scratch.
Solution Approach 2:
The patent maintains continuity of useful action by processing data continuously through the pipeline: LiDAR data continuously updates the 3D map and trajectory, which in turn continuously guides the SFM refinement process. This continuous processing ensures that the system adapts to changing environments in real-time while maintaining precision without requiring complete re-processing of all data.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the creation of a precise 3D feature map for large-scale indoor spaces, allowing for accurate visual localization and overcoming the limitations of existing technologies by providing high-density image sampling and well-aligned 3D models with camera pose information.
Implementation Method 1
collecting spatial data and image data on a specific space by using a lidar sensor and a camera sensor
Implementation Method 2
collecting spatial data and image data on a specific space by using a lidar sensor and a camera sensor
Data Source
AI summary
The present invention relates to a method and system of generating a three-dimensional (3D) map. The 3D map generating method of the present invention includes: collecting spatial data and image data on a specific space by using a lidar sensor and a camera sensor that are each provided on a collecting device; estimating a movement trajectory of the lidar sensor by using the spatial data; and generating 3D structure data on the specific space based on structure-from-motion (SFM), by using the image data and the movement trajectory as input data.