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The paper introduces Ufil, a unified open-source C++/ROS 2 framework for infrastructure-based localization that standardizes object models and provides reusable multi-object tracking components. Ufil decouples perception, tracking, and middleware, enabling modular development and experimentation within localization pipelines. The framework is validated by fusing data from heterogeneous sources (vehicle onboard units, lidar, and in-road sensors) in both CARLA simulation and a physical testbed, achieving lane-level lateral accuracy and sub-100ms latency.
Stop reimplementing localization pipelines: Ufil offers a unified, open-source framework for infrastructure-based localization that lets you swap in new components without rewriting everything.
Infrastructure-based localization enhances road safety and traffic management by providing state estimates of road users. Development is hindered by fragmented, application-specific stacks that tightly couple perception, tracking, and middleware. We introduce Ufil, a Unified Framework for Infrastructure-Based Localization with a standardized object model and reusable multi-object tracking components. Ufil offers interfaces and reference implementations for prediction, detection, association, state update, and track management, allowing researchers to improve components without reimplementing the pipeline. Ufil is open-source C++/ROS 2 software with documentation and executable examples. We demonstrate Ufil by integrating three heterogeneous data sources into a single localization pipeline combining (i) vehicle onboard units broadcasting ETSI ITS-G5 Cooperative Awareness Messages, (ii) a lidar-based roadside sensor node, and (iii) an in-road sensitive surface layer. The pipeline runs unchanged in the CARLA simulator and a small-scale CAV testbed, demonstrating Ufil's scale-independent execution model. In a three-lane highway scenario with 423 and 355 vehicles in simulation and testbed, respectively, the fused system achieves lane-level lateral accuracy with mean lateral position RMSEs of 0.31 m in CARLA and 0.29 m in the CPM Lab, and mean absolute orientation errors around 2.2{\deg}. Median end-to-end latencies from sensing to fused output remain below 100 ms across all modalities in both environments.
