A Neurosymbolic Approach to Adaptive Feature Extraction in SLAM

TL;DR

This paper introduces a neurosymbolic method for adaptive feature extraction in SLAM, combining domain knowledge and learning to improve accuracy and adaptability in changing environments.

Contribution

It presents a novel neurosymbolic architecture and domain-specific language for adaptive feature extraction in SLAM, outperforming traditional methods in accuracy and flexibility.

Findings

Reduced pose error by up to 90% compared to baseline extractors.

Improved feature quality and system adaptability in dynamic environments.

Demonstrated effectiveness of the neurosymbolic approach in real-world scenarios.

Abstract

Autonomous robots, autonomous vehicles, and humans wearing mixed-reality headsets require accurate and reliable tracking services for safety-critical applications in dynamically changing real-world environments. However, the existing tracking approaches, such as Simultaneous Localization and Mapping (SLAM), do not adapt well to environmental changes and boundary conditions despite extensive manual tuning. On the other hand, while deep learning-based approaches can better adapt to environmental changes, they typically demand substantial data for training and often lack flexibility in adapting to new domains. To solve this problem, we propose leveraging the neurosymbolic program synthesis approach to construct adaptable SLAM pipelines that integrate the domain knowledge from traditional SLAM approaches while leveraging data to learn complex relationships. While the approach can synthesize end-to-end SLAM pipelines, we focus on synthesizing the feature extraction module. We first devise a domain-specific language (DSL) that can encapsulate domain knowledge on the important attributes for feature extraction and the real-world performance of various feature extractors. Our neurosymbolic architecture then undertakes adaptive feature extraction, optimizing parameters via learning while employing symbolic reasoning to select the most suitable feature extractor. Our evaluations demonstrate that our approach, neurosymbolic Feature EXtraction (nFEX), yields higher-quality features. It also reduces the pose error observed for the state-of-the-art baseline feature extractors ORB and SIFT by up to 90% and up to 66%, respectively, thereby enhancing the system's efficiency and adaptability to novel environments.