Indoor Mapping Using VSLAM and Ray Casting
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Solution Overview
Problem
Existing SLAM algorithms, such as laser SLAM, suffer from accumulative errors that lead to significant deformation of indoor maps, making them unreliable for navigation and localization, especially in complex environments.
Innovation Solution
A method and device utilizing a Visual SLAM algorithm that acquires initial poses, feature points, and obstacle distances through a combination of VSLAM and ray casting algorithms, with filtering techniques to create accurate indoor environment maps by optimizing pose calculations and feature point locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If laser SLAM algorithm is used for indoor mapping, then mapping speed is improved, but map accuracy deteriorates due to accumulative errors
Solution Approach 1:
The patent combines VSLAM algorithm with ray casting algorithm to create a hybrid system. VSLAM provides fast feature-based mapping while ray casting provides geometric constraint verification. This merging allows the system to maintain both high mapping speed and improved accuracy by cross-validating results from both algorithms.
Solution Approach 2:
The system implements feedback mechanisms where the ray casting algorithm continuously verifies the geometric consistency of VSLAM-generated maps. When deviations are detected, the system adjusts the mapping parameters and reprocesses affected regions, creating a closed-loop correction system that maintains accuracy while preserving speed.
2Productivity
If VSLAM algorithm is used without geometric constraints, then mapping speed is improved, but map reliability deteriorates in complex environments
Solution Approach 1:
The patent performs preliminary geometric constraint analysis using ray casting before finalizing the map. The system pre-calculates expected geometric relationships and uses them as validation criteria during mapping, ensuring that complex environmental features conform to logical spatial expectations before being incorporated into the final map.
Solution Approach 2:
The system dynamically adjusts mapping parameters based on environmental complexity detection. In complex environments, the ray casting algorithm increases the weight of geometric constraints and adjusts feature tracking sensitivity. This parameter adaptation allows the system to maintain reliability in challenging scenarios without sacrificing overall mapping speed.
Data Source
AI summary
A method includes: acquiring a second initial pose according to obstacle region information and geometric scale information of an indoor architectural structure drawing and a first initial pose; acquiring a first feature point location, a locating error and a first pose through a VSLAM algorithm during movement; acquiring a second pose according to the first pose, first initial pose and second initial pose; acquiring a distance to an obstacle according to the second pose, indoor architectural structure drawing and a ray casting algorithm; acquiring a third pose according to the distance, first feature point location, locating error, first pose and a filtering algorithm; acquiring a second feature point location according to the third pose and VSLAM algorithm; and creating an indoor environment map according to the second feature point location and a key frame set.