LOSS-SLAM: Lightweight Open-Set Semantic Simultaneous Localization and Mapping (2404.04377v1)

Abstract: Enabling robots to understand the world in terms of objects is a critical building block towards higher level autonomy. The success of foundation models in vision has created the ability to segment and identify nearly all objects in the world. However, utilizing such objects to localize the robot and build an open-set semantic map of the world remains an open research question. In this work, a system of identifying, localizing, and encoding objects is tightly coupled with probabilistic graphical models for performing open-set semantic simultaneous localization and mapping (SLAM). Results are presented demonstrating that the proposed lightweight object encoding can be used to perform more accurate object-based SLAM than existing open-set methods, closed-set methods, and geometric methods while incurring a lower computational overhead than existing open-set mapping methods.

• The paper introduces a novel lightweight approach using self-supervised DINO features to enable open-set semantic SLAM.
• It integrates object encoding with geometric data to boost localization accuracy and mapping fidelity.
• Experimental results demonstrate superior performance in both accuracy and efficiency compared to closed-set and geometric SLAM methods.

Exploring Open-Set Semantic SLAM with LOSS-SLAM: A Lightweight Approach

Introduction to Open-Set Semantic SLAM

Simultaneous Localization and Mapping (SLAM) forms the backbone of autonomous navigation systems, enabling robots to understand their environment by concurrently building a map and determining their position within it. The integration of semantic understanding into SLAM, termed Semantic SLAM, further augments these capabilities by associating environmental features with meaningful labels. However, a significant challenge arises when the system encounters objects that were not included in its training dataset, a scenario designated as open-set. Addressing this challenge, this paper introduces a novel method termed LOSS-SLAM (Lightweight Open-Set Semantic SLAM), leveraging self-supervised vision transformer features to efficiently and accurately perform open-set semantic SLAM.

Key Contributions and System Overview

The proposed system intertwines object identification, localization, and encoding, tightly coupled with probabilistic graphical models, to facilitate open-set semantic SLAM. Its contributions are threefold:

1. A lightweight open-set object representation crafted through self-supervised vision transformer features, specifically leveraging DINO (Distilled Image Network Outputs), which aids in augmenting geometric correspondence matching at the object level.
2. An integrated open-set semantic SLAM system that exploits the devised object representation alongside geometric information, enhancing both the accuracy of vehicle positioning and the fidelity of semantic mapping.
3. Pioneering experimental validations demonstrating the system's capability to surpass existing methods in accuracy and efficiency, whether open-set, closed-set, or geometric, while ensuring the generation of comprehensive maps.

Technical Insight and Methodology

At its core, LOSS-SLAM innovates by applying DINO, a self-supervised learning framework that extracts visually and semantically meaningful features from images without requiring labeled data. These features are then clustered to identify objects within the environment. For each object detected, a single encoding vector is derived to represent it in the system. This streamlined approach not only facilitates a lightweight representation but also contributes to a more efficient data association process within the SLAM framework.

The system's efficacy is grounded in its ability to effectively merge these encoded object representations with geometric correspondences. This enhances the SLAM process by improving the accuracy of both localization and mapping tasks, even in scenarios involving previously unseen objects. Moreover, the paper meticulously explores various data association techniques, including max-likelihood, max-mixtures, and expectation-maximization, to optimize the integration of new object observations into the existing map.

Experimental Demonstrations and Findings

The experimental evaluation encompasses data collected from indoor environments using an RGBD sensor and an external motion capture system for ground truth validation, as well as publicly available datasets. These experiments benchmark the proposed system against existing approaches, including closed-set and geometric methods. The findings notably highlight LOSS-SLAM's superior performance in terms of mapping accuracy and computational efficiency. Furthermore, the system exhibits robustness to object variability and environmental complexity, indicating its potential for real-world application.

Future Directions and Conclusion

LOSS-SLAM represents a significant step forward in the domain of open-set semantic SLAM by introducing an efficient, accurate, and scalable approach. Future work could explore the integration of dynamic object handling, multi-robot collaboration, and adaptation to more diverse and challenging environments.

In summary, through a novel object encoding strategy underpinned by self-supervised vision transformers, this paper successfully addresses key challenges in open-set semantic SLAM. The resultant system not only advances the state-of-the-art by enabling more accurate and efficient semantic mapping and localization but also opens new avenues for research and application in autonomous navigation.