Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery (1903.00268v2)

Abstract: To autonomously navigate and plan interactions in real-world environments, robots require the ability to robustly perceive and map complex, unstructured surrounding scenes. Besides building an internal representation of the observed scene geometry, the key insight toward a truly functional understanding of the environment is the usage of higher-level entities during mapping, such as individual object instances. We propose an approach to incrementally build volumetric object-centric maps during online scanning with a localized RGB-D camera. First, a per-frame segmentation scheme combines an unsupervised geometric approach with instance-aware semantic object predictions. This allows us to detect and segment elements both from the set of known classes and from other, previously unseen categories. Next, a data association step tracks the predicted instances across the different frames. Finally, a map integration strategy fuses information about their 3D shape, location, and, if available, semantic class into a global volume. Evaluation on a publicly available dataset shows that the proposed approach for building instance-level semantic maps is competitive with state-of-the-art methods, while additionally able to discover objects of unseen categories. The system is further evaluated within a real-world robotic mapping setup, for which qualitative results highlight the online nature of the method.

• The paper introduces a novel framework that integrates volumetric mapping with instance-aware segmentation using both supervised and unsupervised techniques.
• It employs a three-stage process—segmentation, data association, and map integration—to detect and consistently track both known and novel objects.
• Empirical tests on public datasets and real-world scenarios demonstrate competitive accuracy and enhanced autonomy for robotic perception.

Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery: An Expert Overview

The paper "Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery" authored by Grinvald et al., proposes a robust framework for the autonomous mapping of real-world environments using RGB-D cameras. This approach combines volumetric semantic mapping with the ability to discover three-dimensional object instances, offering significant insights into object-centric mapping for robotics. Particularly of interest to researchers in robotic perception and autonomous navigation, this paper explores a hybrid method that is both innovative and practical.

At its core, the paper introduces a novel method for incrementally building volumetric maps enriched with instance-aware semantics. This is achieved by leveraging both supervised and unsupervised techniques; a per-frame segmentation combines unsupervised geometric processing with instance-aware semantic predictions, which allows for the identification of known scene objects as well as novel object-like entities. Such a combination significantly enhances the capability of robotic systems to navigate and interact with their environment, particularly in open-set conditions where unknown objects are encountered.

The proposed framework works through three primary stages: segmentation, data association, and map integration. Initially, the RGB-D camera data is subjected to a per-frame segmentation that detects and classifies object instances using the Mask R-CNN network alongside geometrically based approaches. This dual process helps mitigate over-segmentation of complex shapes and assigns semantic labels to objects. The data association step is crucial in tracking these segmented objects across different frames, ensuring consistency and accuracy in the map generated. Finally, the integration of these data into a global map is facilitated by extending the TsDF-based mapping provided by Voxblox, which now accommodates dense semantic and instance-wise information.

The empirical evaluations, conducted using publicly available datasets like SceneNN, were complemented by a robotic mapping application under real-world conditions. Within these environments, the framework’s performance was benchmarked against existing state-of-the-art methods, demonstrating competitive mean Average Precision (mAP) scores. The approach effectively discovered unknown object instances without prior semantic knowledge, a substantial advancement over traditional methods limited to fixed classes.

Throughout the experiments, the authors illustrate that their approach not only upholds accuracy comparable to existing semantic segmentation techniques but also significantly augments the capability of the robotic systems to discover novel objects autonomously. An online mapping scenario further displayed the framework’s potential in dynamic environments, reinforcing the paper’s emphasis on real-time applications.

The practical implications for deploying autonomous robots in varied environmental conditions are substantial. The framework’s ability to generate instance-specific semantic maps could greatly enhance robot decision-making processes, facilitate efficient path planning, and improve interactions with environments through task-relevant information. Theoretically, this work bridges the gap between geometric-based mapping and semantic perception, paving the way for future research to improve scene understanding and autonomy in robotics.

Future developments might concentrate on optimizing runtime performance to achieve real-time operation, enlightening how integrating deep learning directly affects computation in practical robotic applications. Furthermore, exploring synergy in multi-sensory fusion could refine object discovery and enhance environmental situational awareness.

Overall, this paper presents a concrete step in advancing robotic perception systems, enabling them to construct and utilize semantically rich maps for more adaptive and contextually aware interaction with their environments.