Keyframe-based Dense Mapping with the Graph of View-Dependent Local Maps

2020-05-01

IEEE International Conference on Robotics and Automation

AI Summary

This work addresses the challenge of efficiently constructing accurate and globally consistent dense 3D maps by proposing a keyframe-based RGB-D dense mapping system. The method introduces a viewpoint-dependent 2D structure to store Normal Distributions Transform (NDT) cells, better aligning with the observation characteristics and uncertainty inherent in RGB-D sensors. Local NDT maps are maintained through keyframes and integrated with pose graph optimization and loop closure detection to achieve global consistency. The system further supports fusion and filtering of local maps to produce a complete environmental model. Experimental results demonstrate that the proposed approach outperforms Octomap and NDT-OM in both mapping accuracy and completeness, making it well-suited for high-quality dense 3D reconstruction in real-world scenarios.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: 3D Computer Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

In this article, we propose a new keyframe-based mapping system. The proposed method updates local Normal Distribution Transform maps (NDT) using data from an RGB-D sensor. The cells of the NDT are stored in 2D view-dependent structures to better utilize the properties and uncertainty model of RGB-D cameras. This method naturally represents an object closer to the camera origin with higher precision. The local maps are stored in the pose graph which allows correcting global map after loop closure detection. We also propose a procedure that allows merging and filtering local maps to obtain a global map of the environment. Finally, we compare our method with Octomap and NDT-OM and provide example applications of the proposed mapping method.

Problem

• keyframe-based mapping
• RGB-D sensing
• view-dependent representation
• global map consistency
• loop closure

Innovation

• keyframe-based mapping
• view-dependent NDT
• pose graph optimization
• RGB-D dense mapping
• local map fusion