EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association (2004.12730v2)

Abstract: Object-level data association and pose estimation play a fundamental role in semantic SLAM, which remain unsolved due to the lack of robust and accurate algorithms. In this work, we propose an ensemble data associate strategy for integrating the parametric and nonparametric statistic tests. By exploiting the nature of different statistics, our method can effectively aggregate the information of different measurements, and thus significantly improve the robustness and accuracy of data association. We then present an accurate object pose estimation framework, in which an outliers-robust centroid and scale estimation algorithm and an object pose initialization algorithm are developed to help improve the optimality of pose estimation results. Furthermore, we build a SLAM system that can generate semi-dense or lightweight object-oriented maps with a monocular camera. Extensive experiments are conducted on three publicly available datasets and a real scenario. The results show that our approach significantly outperforms state-of-the-art techniques in accuracy and robustness. The source code is available on: https://github.com/yanmin-wu/EAO-SLAM.

• The paper introduces an ensemble data association strategy that robustly integrates parametric and nonparametric tests for improved object localization.
• The proposed SLAM framework achieves accurate object pose estimation using robust centroid and scale algorithms alongside iForest initialization.
• Experiments on benchmark datasets demonstrate that EAO-SLAM significantly outperforms conventional methods in mapping accuracy and robustness.

An Overview of EAO-SLAM: Advancements in Monocular Semi-Dense Object SLAM

This paper presents EAO-SLAM, a novel approach to monocular semi-dense object SLAM that leverages ensemble data association strategies for improved object localization and mapping. The proposed methodology addresses the long-standing challenges in semantic SLAM relating to data association and pose estimation, integrating parametric and nonparametric statistical tests to enhance algorithmic robustness and accuracy.

Key Contributions and Methodology

EAO-SLAM integrates multiple innovative strategies to overcome the limitations of traditional SLAM systems when faced with complex environments:

1. Ensemble Data Association: The authors propose a hybrid data association strategy that combines parametric and nonparametric statistical tests, enhancing the system's robustness to environment-induced variabilities. Through this strategy, the SLAM system efficiently aggregates disparate measurement information, substantially improving the accuracy of data associations in environments with multiple object instances.
2. Object Pose Estimation Framework: The system adopts an innovative object pose estimation framework, characterized by outlier-robust centroid and scale estimation algorithms alongside object pose initialization based on the isolation forest (iForest). This approach circumvents the shortcomings of conventional estimation methods that rely heavily on idealized parameter assumptions.
3. Monocular Object SLAM: EAO-SLAM employs a monocular camera setup to generate semi-dense or lightweight maps that effectively represent objects through geometric elements such as cubes and quadrics. Comprehensive experiments across multiple datasets reveal that the system significantly outperforms existing techniques in terms of accuracy and robustness.

Experimental Validation

Extensive experiments conducted on benchmark datasets—TUM, Microsoft RGBD, and Scenes V2—underscore the system's superiority. Notable findings indicate that EAO-SLAM achieves higher rates of accurate object association, compared to traditional IoU and standalone nonparametric methods, thereby advancing the state of the art in SLAM. The authors also provide visualizations of object-oriented maps, showcasing the system's capability to accurately reconstruct environments containing objects with diverse geometries and orientations.

Implications and Future Directions

EAO-SLAM's innovative integration of statistical methods for data association and pose estimation holds significant implications for the broader field of robotic perception and mapping. By mitigating the pitfalls associated with non-Gaussian statistics in multi-object environments, the approach not only refines SLAM accuracy but also bolsters the potential for real-world applicability in robotic navigation and automation tasks. The lightweight and robust mapping capabilities demonstrated by EAO-SLAM support advancements in dynamic robotic interaction scenarios, including manipulation and autonomous navigation in cluttered environments.

Looking ahead, EAO-SLAM lays foundational concepts that can be expanded into multi-camera or even hybrid sensor SLAM systems, further enhancing environmental comprehension and interaction for robots. The ensemble approach to data association can stimulate new research into blending various forms of sensory data to adaptively optimize Slam accuracy in real-time.

In conclusion, this paper presents a substantial contribution to the field of semantic SLAM by introducing an ensemble framework that effectively leverages statistical strengths for improved data association and pose estimation, positioning it as a pivotal step forward in achieving autonomous robotic perception and interaction capabilities.