Dual-LiDAR Navigation Mapping for Dynamic Obstacle Positioning
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Solution Overview
Problem
In dynamic environments with movable obstacles, existing SLAM technologies face challenges in maintaining accurate robot positioning due to rapid changes in obstacle positions, leading to low positioning accuracy.
Innovation Solution
The use of a first lidar positioned away from the ground and a second lidar close to the ground on a robot, where the first lidar constructs a map based on its data and calculates positioning data for the second lidar, and both maps are fused to generate a navigation map that includes obstacle information, enhancing positioning accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single lidar is used for SLAM mapping, then the device complexity is low, but the positioning accuracy deteriorates in dynamic environments with movable obstacles
Solution Approach 1:
The patent divides the mapping function into two segments: a first lidar (above ground) constructs a static map while a second lidar (near ground) constructs a dynamic map. This segmentation allows each lidar to specialize in detecting specific types of obstacles, resolving the contradiction between maintaining low device complexity and improving positioning accuracy in dynamic environments.
Solution Approach 2:
The patent introduces a vertical dimension by positioning the first lidar above ground level and the second lidar near ground level. This dimensional separation enables the system to capture obstacle information from different heights, improving positioning accuracy without significantly increasing horizontal device complexity.
2Reliability
If only one map is constructed from a single lidar, then the processing time is short, but the navigation reliability deteriorates due to inability to account for movable objects
Solution Approach 1:
The patent segments the mapping process into parallel static map construction and dynamic map construction, allowing both maps to be built simultaneously from different lidars. This parallel processing maintains reasonable construction time while improving navigation reliability through fused multi-source information.
Solution Approach 2:
The patent merges the static map from the first lidar with the dynamic map from the second lidar to create a comprehensive navigation map. This combination integrates both stable environmental structures and movable obstacles, enhancing navigation reliability without excessive time loss.
3Measurement precision
If a single lidar constructs the navigation map, then the device structure is simple, but the obstacle detection precision deteriorates in environments with rapid obstacle position changes
Solution Approach 1:
The patent applies local quality by assigning different detection specialties to different lidars: the first lidar (above ground) optimally detects elevated obstacles while the second lidar (near ground) optimally detects ground-level movable obstacles. This localized optimization of detection quality improves overall obstacle detection precision without requiring each lidar to be overly complex.
Solution Approach 2:
The patent segments the obstacle detection function across two lidars positioned at different heights, with each lidar responsible for detecting obstacles in its optimal detection zone. This segmentation improves obstacle detection precision for rapid position changes while keeping individual lidar structures relatively simple.
Data Source
AI summary
A storage medium, a robot, and a method for generating navigation map are provided. By disposing a first lidar and a second lidar located higher than the first lidar, it constructs a first map corresponding to the first lidar based on first laser data collected by the first lidar, and calculate second positioning data corresponding to the second lidar during constructing the first map, constructs a second map corresponding to the second lidar based on the second positioning data and second laser data collected by the second lidar, and obtains a navigation map corresponding to the robot by fusing the first map with the second map, such that the fused map includes not only positioning information provided by the first map, but also obstacle information provided by the first map and the second map.