PyRoboVision: A Modular Python Framework for Multi-Object Tracking, Trajectory Prediction, and Robotic Perception

AbstractPyRoboVision is an open-source Python framework providing modular capabilities for multi-object tracking, trajectory prediction, and supporting perception and sensor-processing tasks in robotics and autonomous systems. It is designed to provide reusable components that can be integrated into larger robotic perception pipelines without requiring a monolithic end-to-end implementation. The core tracking module combines Kalman-filter-based state estimation with Hungarian-algorithm data association to maintain object identities across successive observations. It supports track lifecycle management, handling of temporary detection gaps, and re-association following occlusion. The trajectory prediction module provides constant-velocity and constant-acceleration models with uncertainty estimates to support downstream motion analysis and robotic decision-making. Additional components provide utilities for monocular depth estimation using an optional MiDaS backend, three-dimensional bounding-box conversion, LiDAR point-cloud processing, occupancy-grid representation, IMU/GPS sensor fusion, rule-based behavior and intent classification, and basic imitation-learning and safety-constraint functionality. Architecture and Implementation PyRoboVision is implemented in Python, with NumPy and SciPy supporting its core tracking and numerical operations. PyTorch is an optional dependency for selected deep-learning functionality. The modular package structure allows individual components to be used independently and integrated with external object detectors, datasets, and robotic applications. Scope and Limitations PyRoboVision is a perception and motion-analysis framework rather than a complete autonomous-driving or autonomous-robotics stack. Object detection is supplied externally, and the tracking and prediction components do not by themselves constitute a validated collision-avoidance system. Monocular depth estimates require appropriate calibration when metric depth is needed. The framework's suitability for a particular application depends on its operating conditions and independent evaluation. Availability Source code, installation instructions, usage examples, and development documentation are available at: https://github.com/Mullassery/PyRoboVision The project is intended to support reusable robotics software development, experimentation, and further evaluation of modular perception and motion-prediction components.