RO-MAP: Real-Time Multi-Object Mapping with Neural Radiance Fields (2304.05735v2)

Abstract: Accurate perception of objects in the environment is important for improving the scene understanding capability of SLAM systems. In robotic and augmented reality applications, object maps with semantic and metric information show attractive advantages. In this paper, we present RO-MAP, a novel multi-object mapping pipeline that does not rely on 3D priors. Given only monocular input, we use neural radiance fields to represent objects and couple them with a lightweight object SLAM based on multi-view geometry, to simultaneously localize objects and implicitly learn their dense geometry. We create separate implicit models for each detected object and train them dynamically and in parallel as new observations are added. Experiments on synthetic and real-world datasets demonstrate that our method can generate semantic object map with shape reconstruction, and be competitive with offline methods while achieving real-time performance (25Hz). The code and dataset will be available at: https://github.com/XiaoHan-Git/RO-MAP

• The paper introduces a novel pipeline that integrates neural radiance fields with object-oriented SLAM to achieve real-time mapping without relying on predefined 3D models.
• It employs dynamic, parallel training to learn detailed object geometries from monocular visual input, achieving up to 25Hz processing speed.
• The approach optimizes loss functions for efficient convergence and accurate reconstruction, reducing hardware requirements for robotics and AR applications.

Real-Time Multi-Object Mapping with Neural Radiance Fields

The paper "RO-MAP: Real-Time Multi-Object Mapping with Neural Radiance Fields" introduces an innovative approach to visual Simultaneous Localization and Mapping (SLAM) that integrates neural radiance fields (NeRFs) with object-oriented data processing to enhance real-time scene understanding. This research is particularly relevant for fields such as robotics and augmented reality, where precise and contextual object mapping can significantly improve automated navigation, object manipulation, and scene interaction.

Technical Contributions

RO-MAP is proposed as a novel pipeline enabling multi-object mapping without reliance on predefined 3D models. Utilizing monocular visual input, the method focuses on dynamically learning the geometries of objects using neural radiance fields. The novelty of the approach lies in several key aspects:

• Object Representation Without 3D Priors: Unlike many existing methodologies that depend on category-specific shape priors, this approach leverages NeRFs to capture object shape and texture from RGB data alone. This enables the system to accommodate arbitrary object geometries that deviate from standard forms like cuboids or ellipsoids.
• Parallel and Dynamic Training: RO-MAP introduces a method for maintaining individual implicit models for each detected object. These models are trained in real-time as new data is captured, allowing the representation to adapt dynamically to the observed environment.
• Efficient Loss Functions: The paper proposes optimized loss functions for object modeling, enhancing both the convergence speed of the network and the accuracy of the learned representations.

Experimental Evaluation

The authors validate their approach on both synthetic and real-world datasets, providing evidence that RO-MAP can perform comparably to offline methods while maintaining real-time capabilities (achieving up to 25Hz). Key experimental results showed that the method could maintain competitive reconstruction quality even under the constraint of monocular RGB input, a traditionally challenging scenario for geometry learning due to depth ambiguity.

Implications

The implications of this research span theoretical and practical dimensions. Theoretically, the integration of NeRFs into real-time systems exemplifies a maturation of neural representation techniques moving towards practical applicability. From a practical standpoint, achieving accurate object mapping without the need for depth sensors or pre-trained shape models reduces the resource requisites for robotic platforms, potentially lowering cost and expanding applicability.

Forward-Looking Considerations

The trajectory outlined by this work suggests substantive progress towards generalizable SLAM systems capable of semantic understanding. Future research could explore:

• Integration with Dynamic Scenes: Extending capabilities to handle dynamic environments with moving objects could broaden the system's utility in real-world applications.
• Cross-Domain Adaptability: Investigating transferability across different domains or incorporating multi-modal sensor data could help to exploit the strengths of various sensory inputs.
• Resource Optimization: Further optimal continual learning strategies for the incrementally growing dataset could offer efficiency improvements, making the system viable on resource-limited hardware common in robotics.

In summary, "RO-MAP" presents a step forward in object-oriented SLAM methodologies, enhancing scene perception with neural implicit representations that remain flexible and exact without reliance on predefined geometry, offering an adaptable framework with broad implications for autonomous applications.