  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.
