External Rotation 3D Lidar for SLAM Localization Accuracy
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Solution Overview
Problem
Existing hybrid solid-state lidars with small fields of view suffer from limited sensing capabilities, large localization errors, and low mapping efficiency when applied to robots, posing challenges for SLAM algorithms.
Innovation Solution
An external rotation 3D lidar device and a SLAM method that combines error-state iterated Kalman filtering with pose graph optimization, enabling 360-degree environment sensing and correcting cumulative errors through loop-closure optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of stationary object
If a hybrid solid-state lidar with small field of view is used, then long-distance detection capability is improved, but sensing capability and localization effect deteriorate
Solution Approach 1:
The patent introduces a rotating mechanism that adds temporal dimension to the scanning process. The lidar performs multiple scanning cycles at different angular positions, transforming a static small FoV system into a dynamic 360-degree coverage system through multi-dimensional scanning patterns.
Solution Approach 2:
The system transitions from a static lidar mounting to a dynamic rotating structure. The rotation angle and scanning patterns are adjustable, allowing the system to adapt to different environmental conditions and robot motion states, thereby improving both sensing coverage and localization accuracy.
2Length of stationary object
If a hybrid solid-state lidar with small field of view is used, then long-distance detection capability is improved, but mapping efficiency deteriorates
Solution Approach 1:
The system performs preliminary scanning at multiple angular positions before completing the full mapping process. By pre-collecting data from different angles and using predictive algorithms to estimate robot pose, the system prepares information in advance that accelerates the overall mapping efficiency.
Solution Approach 2:
The system uses feedback from previously scanned data to guide subsequent scanning operations. The pose estimation results from earlier scanning cycles inform the planning of future scans, allowing the system to optimize its scanning pattern and reduce redundant measurements, thereby improving mapping efficiency.
3Manufacturing precision
If feature extraction based on scanning characteristics is used, then point cloud processing is improved, but localization effect in unstructured environments deteriorates
Solution Approach 1:
The system dynamically adjusts processing parameters based on the detected environment type. In unstructured environments, it switches from feature-based processing to direct point cloud registration methods, changing the algorithmic parameters to match the environmental characteristics and maintain localization accuracy.
Solution Approach 2:
The localization algorithm transitions from a static feature extraction approach to a dynamic method that adapts its processing strategy based on real-time environmental assessment. The system can switch between different processing modes (feature-based vs. direct registration) depending on the structure and complexity of the environment.
4Measurement precision
If tightly-coupled lidar inertial odometry is used, then pose accuracy is improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The patent introduces a slip ring as an intermediary component that enables the rotation mechanism to function. The slip ring provides continuous electrical connection while allowing rotational movement, simplifying the overall system architecture compared to alternative solutions like wireless power transmission or complex rotary joints.
5Adaptability or versatility
If external rotation mechanism is added to achieve 360-degree sensing, then sensing capability is improved, but device complexity increases
Solution Approach 1:
The rotating mechanism serves multiple functions simultaneously: it provides 360-degree sensing coverage, enables the lidar to track moving targets, and allows the system to adapt to different scanning patterns. This multi-functionality justifies the added complexity by delivering multiple performance benefits from a single structural addition.
Data Source
AI summary
An external rotation 3D lidar device and a simultaneous localization and mapping (SLAM) method comprises a 3D hybrid solid-state lidar device that is driven to rotate by an external motor. The device significantly improves the horizontal field of view of the lidar and can be mounted on a ground robot to comprehensively improve its 360-degree environment sensing capabilities. Error-state Kalman filtering and pose graph optimization are combined and the overall framework is divided into two parts: front-end odometry and back-end loop-closure optimization. Therefore, high-frequency odometry that meets the requirements of the robot can be output in real time and cumulative errors can be eliminated through the back-end loop-closure optimization.