Constructing Category-Specific Models for Monocular Object-SLAM (1802.09292v1)

Abstract: We present a new paradigm for real-time object-oriented SLAM with a monocular camera. Contrary to previous approaches, that rely on object-level models, we construct category-level models from CAD collections which are now widely available. To alleviate the need for huge amounts of labeled data, we develop a rendering pipeline that enables synthesis of large datasets from a limited amount of manually labeled data. Using data thus synthesized, we learn category-level models for object deformations in 3D, as well as discriminative object features in 2D. These category models are instance-independent and aid in the design of object landmark observations that can be incorporated into a generic monocular SLAM framework. Where typical object-SLAM approaches usually solve only for object and camera poses, we also estimate object shape on-the-fly, allowing for a wide range of objects from the category to be present in the scene. Moreover, since our 2D object features are learned discriminatively, the proposed object-SLAM system succeeds in several scenarios where sparse feature-based monocular SLAM fails due to insufficient features or parallax. Also, the proposed category-models help in object instance retrieval, useful for Augmented Reality (AR) applications. We evaluate the proposed framework on multiple challenging real-world scenes and show --- to the best of our knowledge --- first results of an instance-independent monocular object-SLAM system and the benefits it enjoys over feature-based SLAM methods.

• The paper introduces an object-SLAM framework that uses category-specific models derived from CAD data, synthesizing training data via rendering pipelines.
• The proposed system leverages linear subspace models for object categories and CNNs for 2D feature extraction, generating synthetic data for improved precision.
• Evaluations show superior performance over feature-based SLAM, with improved object localization and trajectory accuracy, demonstrating benefits for augmented reality applications.

Overview of Monocular Object-SLAM with Category-Specific Models

The paper "Constructing Category-Specific Models for Monocular Object-SLAM" presents an innovative approach to enhancing simultaneous localization and mapping (SLAM) systems through object-specific information using monocular cameras. This research augments traditional SLAM methods by integrating category-level models derived from CAD collections, thereby addressing limitations associated with instance-specific models and generic object representations.

Core Contributions

The authors propose a framework that synthesizes data from limited manually labeled sources, using rendering pipelines to generate expansive training datasets. This facilitates the learning of category-level models capable of discerning object deformations in three dimensions and extracting discriminative features in two dimensions. The models designed are instance-independent and introduce object landmark observations into a monocular SLAM framework. This expands SLAM capabilities by not only estimating the object and camera poses but also dynamically assessing object shapes as the scene evolves. Such advances in object-SLAM can prove robust in scenarios that challenge traditional monocular SLAM methods due to sparse features or parallax.

Methodology and Pipeline

The implemented approach assimilates category-specific models while operating within SLAM factor graphs. The paper highlights the utilization of linear subspace models to depict object categories, coupled with convolutional neural networks for precise 2D object feature extraction. A significant highlight is the deployment of a customized rendering pipeline, which allows the generation of synthetic training data from modestly annotated sources. This approach epitomizes efficient data usage, enhancing the precision of learned feature detectors over those trained solely on real-world datasets.

Results and Evaluation

Empirical evaluations on various real-world sequences illustrate the potential of the proposed system to yield first-instance-independent monocular object-SLAM results. The framework shows superior performance over feature-based SLAM methods, notably in rotational conditions that typically challenge monocular SLAM. Numerical evaluations reveal significant improvements in object localization errors and trajectory drift corrections, showcasing the benefits of integrating object loop closures in SLAM processes.

Implications and Future Work

The proposed models hold promising applications in augmented reality, particularly in embedding object models within real-time scenes. The instance-independent nature of the system suggests its adaptability across a range of rigid object categories, contingent upon the availability of aligned CAD data. Future exploration may focus on diminishing the dependence on supervised data for category model training, thus extending the applicability and efficiency of monocular object-SLAM systems.

This research opens pathways for further investigations into the synergy between object-category modeling and SLAM, reinforcing the paper's contributions to the evolving landscape of robotics and autonomous navigation.