Dual-LiDAR Robot Mapping for Stable Navigation Around Movable Objects
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Solution Overview
Problem
Existing SLAM methods for robots face instability in positioning and navigation due to movable objects like tables and chairs, especially when using dual radars, leading to increased computation and memory demands and positional jittering.
Innovation Solution
A mapping method using dual lidars installed at different heights on a robot, where one lidar is higher to avoid movable objects and provides stable positioning, while the other detects them, with data fusion to create a unified map for navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dual lidars are used for positioning and navigation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system divides the lidar detection task into two segments: one lidar (first lidar) is dedicated to detecting stationary objects for positioning, while the other lidar (second lidar) detects movable objects. This segmentation allows each sensor to focus on specific detection goals, improving overall positioning precision without requiring a single complex system to handle all detection tasks simultaneously.
2Ease of operation
If traditional SLAM methods are used in environments with movable objects, then positioning is performed, but reliability deteriorates due to environmental changes
Solution Approach 1:
The system extracts and separates the detection of movable objects from the positioning process. By using the second lidar to specifically detect movable objects and excluding them from the positioning calculation based on the first lidar's data, the system removes the source of instability caused by environmental changes, thereby maintaining reliable and stable positioning even in dynamic environments.
3Measurement precision
If dual lidars detect all objects including movable objects, then measurement precision is improved, but loss of time increases due to re-detection needs
Solution Approach 1:
The system applies different detection qualities and processing levels to different types of objects. Stationary objects detected by the first lidar are processed with high precision for positioning, while movable objects detected by the second lidar are identified and excluded from positioning calculations. This local differentiation in processing quality avoids the time loss associated with re-detecting and re-processing movable objects while maintaining accurate positioning based on stationary objects.
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
The method stabilizes positioning and navigation by effectively bypassing movable objects, enhancing map accuracy and reducing computational overhead.
Implementation Method 1
a first lidar installed at a first position on the robot and a second lidar installed at a second position on the robot, where a height of the first position is higher than a height of the second position
Implementation Method 2
obtaining a first laser key frame of a stationary object in a current environment by the first lidar; obtaining a second laser key frame of a movable object in the current environment by the second lidar
Data Source
AI summary
A mapping method, a computer-readable storage medium, and a robot are provided. The method is applied to a robot including a first lidar and a second lidar, where the first lidar is installed at a position higher than that of the second lidar. The method includes: obtaining a first laser key frame; calculating a current pose of a robot based on the first laser key frame; updating a first probability map based on the current pose and the first laser key frame; obtaining a second laser key frame; updating a second probability map based on the current pose and the second laser key frame; and generating a fused grid map for navigating the robot based on the updated first probability map and the updated second probability map, thereby greatly improving the stability of positioning and navigation.