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RGB-D SLAM Using Point-Plane Constraints for Indoor Environments.
Ruibin Guo1, Keju Peng2, Weihong Fan3
1College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China. guoruibin08@nudt.edu.cn.
Sensors (Basel, Switzerland)
|June 20, 2019
Summary
This study introduces a novel point-plane method for robots to simultaneously estimate poses and reconstruct maps using RGB-D cameras, improving autonomous navigation in challenging environments.
Area of Science:
- Robotics
- Computer Vision
- Simultaneous Localization and Mapping (SLAM)
Background:
- Robotic autonomous behavior relies on accurate pose estimation and environment mapping.
- Existing methods struggle in low-texture environments and require robust feature detection.
Purpose of the Study:
- To develop a robust point-plane-based method for simultaneous pose estimation and map reconstruction.
- To enhance accuracy in low-texture scenes and Manhattan World environments.
Main Methods:
- Detecting and tracking point and plane features from RGB-D images.
- Constructing cost functions using a plane's minimal representation for optimization.
- Incorporating Manhattan World (MW) constraints for improved pose estimation.
Main Results:
- The proposed algorithm demonstrates robust and accurate pose estimation and map reconstruction.
- Effective performance is shown even in low-texture scenes.
- Significant advantages are observed compared to alternative methods.
Conclusions:
- The point-plane-based method offers a reliable solution for robotic pose estimation and map reconstruction.
- The integration of MW constraints further enhances accuracy in structured environments.
- The algorithm is validated on public RGB-D datasets, proving its practical applicability.
Keywords:
Manhattan WorldRGB-D cameramap reconstructionpoint–plane-based factor graphpose estimationvisual SLAMMore Related Videos
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