PLVS: A SLAM System with Points, Lines, Volumetric Mapping, and 3D Incremental Segmentation (2309.10896v2)

Abstract: This document presents PLVS: a real-time system that leverages sparse SLAM, volumetric mapping, and 3D unsupervised incremental segmentation. PLVS stands for Points, Lines, Volumetric mapping, and Segmentation. It supports RGB-D and Stereo cameras, which may be optionally equipped with IMUs. The SLAM module is keyframe-based, and extracts and tracks sparse points and line segments as features. Volumetric mapping runs in parallel with respect to the SLAM front-end and generates a 3D reconstruction of the explored environment by fusing point clouds backprojected from keyframes. Different volumetric mapping methods are supported and integrated in PLVS. We use a novel reprojection error to bundle-adjust line segments. This error exploits available depth information to stabilize the position estimates of line segment endpoints. An incremental and geometric-based segmentation method is implemented and integrated for RGB-D cameras in the PLVS framework. We present qualitative and quantitative evaluations of the PLVS framework on some publicly available datasets. The appendix details the adopted stereo line triangulation method and provides a derivation of the Jacobians we used for line error terms. The software is available as open-source.

• The paper introduces PLVS, a SLAM system that integrates sparse point and line tracking with volumetric mapping and 3D segmentation for enhanced environment reconstruction.
• It employs a keyframe-based architecture with a novel reprojection error to stabilize line segments using depth information from RGB-D and stereo cameras.
• The open-source modular design allows customization for robust, real-time mapping on resource-constrained robotic platforms.

Overview of PLVS: A SLAM System with Points, Lines, Volumetric Mapping, and 3D Incremental Segmentation

The paper presents PLVS, a sophisticated real-time SLAM framework developed to effectively integrate sparse SLAM, volumetric mapping, and 3D unsupervised incremental segmentation. Targeting both RGB-D and stereo cameras, the system is adaptable to devices fitted with IMUs. PLVS, an acronym for Points, Lines, Volumetric mapping, and Segmentation, aims to robustly function on small robotic platforms.

The SLAM module employs a keyframe-based architecture to extract and track points and line segments, providing a sparse feature map. Parallel to this, volumetric mapping fuses these keyframe-derived point clouds for a comprehensive 3D reconstruction, supporting multiple volumetric methods like octrees and TSDFs. A novel reprojection error aids in line segment stabilization using depth data. The PLVS framework further incorporates a geometry-based segmentation technique.

System Components

PLVS consists of two main subsystems:

1. Sparse SLAM: Based on ORB-SLAM, the subsystem tracks and maps sparse points and line segments. It is available in two versions: PLVS I (using ORB-SLAM2) and PLVS II (using ORB-SLAM3, supporting IMUs).
2. Volumetric Mapping: Operating concurrently with sparse SLAM, it reconstructs the 3D environment using various methods to combine point clouds backprojected from keyframes.

Additionally, the open-source nature of PLVS allows for high levels of customization, enabling users to balance map resolution against computational load.

Key Features and Innovations

• Reprojection Error: Provides enhanced estimates of line segment endpoints by leveraging available depth information, applicable to both RGB-D and stereo systems.
• Incremental Segmentation: Geometry-based and unsupervised, it is currently implemented for RGB-D setups.
• Modular Design: Users can enable or disable different functionalities to tailor the system’s capabilities to specific requirements.

Numerical Evaluations

The paper includes both qualitative and quantitative evaluations, demonstrating the framework's performance across publicly available datasets. The results suggest that PLVS achieves localization and reconstruction accuracy on par with established SLAM systems, maintaining efficient CPU usage to facilitate deployment on mobile robots with limited resources.

Implications and Future Directions

Practically, PLVS offers a versatile solution for robotic applications requiring real-time environmental interaction. Theoretically, it provides a foundation for further paper in the stabilization of line segments within SLAM frameworks.

Future developments may extend PLVS to incorporate advanced machine learning models for improved scene understanding and adaptation. The integration of additional sensor types and AI-driven segmentation could further enhance the system's application range, from autonomous vehicles to drone-based exploration.

In summary, while PLVS does not introduce revolutionary concepts, it represents a significant step in creating a multi-functional, adaptable SLAM framework suitable for real-world robotic applications. Researchers and developers may benefit from its open-source availability, contributing to continued innovation in robotic perception systems.