Mobile Pose Estimation Using DVS and Radar in Complex Lighting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current pose estimation methods using RGB and IMU sensors suffer from low accuracy in complex lighting conditions, such as excessively strong or dim light.
Innovation Solution
Integrate a dynamic vision sensor (DVS) and a single-line lidar to enhance feature extraction and fusion, leveraging the DVS's ability to capture dynamic changes and the lidar's robustness to light intensity, performing feature enhancement and alignment to improve pose estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If RGB color sensor and IMU are used for pose estimation, then the system structure is simple, but pose estimation accuracy deteriorates in complex lighting conditions
Solution Approach 1:
The patent combines DVS (dynamic vision sensor) and single-line lidar into a unified sensing system, where DVS captures dynamic visual changes and lidar provides depth information. This merged system compensates for the weaknesses of individual sensors in complex lighting conditions, achieving high-accuracy pose estimation without significantly increasing system complexity
Solution Approach 2:
The patent creates a composite sensing system by integrating different sensing technologies (DVS and lidar) with complementary characteristics. DVS excels in capturing motion dynamics while lidar provides light-intensity-independent depth measurement, forming a robust composite system that maintains accuracy across varying lighting environments
2Measurement precision
If DVS and single-line lidar are integrated for sensing, then pose estimation accuracy in complex environments is improved, but device complexity increases
Solution Approach 1:
The patent introduces a feature enhancement module as an intermediary that processes raw sensing data from DVS and lidar. This mediator performs feature extraction, matching, and fusion operations, transforming complex multi-source data into reliable pose estimates while managing the computational complexity systematically
Solution Approach 2:
The patent segments the pose estimation process into distinct functional modules: DVS data processing, lidar data processing, feature enhancement, feature matching, and pose calculation. This segmentation allows each module to be optimized independently and simplifies the overall system architecture despite the advanced sensing capabilities
3Measurement precision
If feature enhancement is performed on depth information, then pose estimation accuracy is improved, but computational energy consumption increases
Solution Approach 1:
The patent applies feature enhancement selectively rather than uniformly to all depth information. By focusing computational resources on enhancing critical features that most impact pose estimation accuracy and using threshold-based filtering to discard low-value data, the system achieves high accuracy while avoiding unnecessary computational energy consumption
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
Enhances pose estimation accuracy in complex environments by complementing DVS and lidar data, ensuring precise determination of a mobile apparatus's pose regardless of lighting conditions, while also reducing power consumption and costs.
Implementation Method 1
obtaining first sensing data, where the first sensing data is obtained by using a dynamic vision sensor (DVS) of the mobile apparatus by detecting physical space in which the mobile apparatus is located
Implementation Method 2
the second sensing data is obtained by using a single-line light detection and ranging (lidar) of the mobile apparatus by detecting the physical space in which the mobile apparatus is located
Data Source
AI summary
A pose estimation method and a related apparatus are provided. The method includes: obtaining first sensing data and second sensing data, where the first sensing data is obtained using a DVS of a mobile apparatus by detecting a physical space in which the mobile apparatus is located, and the second sensing data is obtained using a 2D radar of the mobile apparatus by detecting the physical space in which the mobile apparatus is located; performing feature enhancement on first depth information based on the first sensing data, to obtain second depth information, where the first depth information is obtained by fusing the first sensing data and the second sensing data; and determining a pose of the mobile apparatus based on the second depth information.