Master Mix Localization Algorithm for Autonomous Systems in Indoor Environments

Reliable navigation in GPS-denied environments remains a critical challenge for autonomous vehicles (AVs), particularly in urban canyons, tunnels, and areas with adverse weather. GPS-based localization systems often fail under these conditions, highlighting the need for resilient, multimodal solutions. This paper introduces a radar-assisted localization approach that combines LiDAR, Inertial Measurement Units (IMUs), and deep learning techniques for robust navigation. The proposed approach combines sensor fusion, Kalman filtering, Gaussian Mixture Models (GMM), and Nonlinear AutoRegressive with eXogenous inputs (NARX) system identification within a unified probabilistic framework. The framework provides continuous real-time localization and environment mapping while enabling short-horizon prediction of the slave robot’s motion based on master robot observations. By forecasting the slave robot’s future states several seconds ahead, the method enhances tracking accuracy, coordination, and overall system robustness in dynamic environments. By fusing sensor data through a Convolutional Recurrent Neural Network (CRNN), our system maintains reliable mapping even in GPS-challenging environments. Radar’s ability to detect objects through weather obstructions, LiDAR’s high-resolution spatial mapping, and IMU data for real-time vehicle tracking work together to reduce the limitations of each individual sensor. To enhance accuracy, we apply Kalman filtering for state estimation and prediction within the GMM-based framework, rather than solely for post-processing or trajectory smoothing, addressing sensor noise and drift. Through simulations and real-world indoor data, we demonstrate the system’s ability to deliver accurate, continuous pose estimation, even in challenging conditions, with low localization errors characterized by RMSE values of 0.12 m, 0.15 m, and 0.28 m along the X, Y, and Z axes, respectively, and sub-meter maximum position deviations. This work represents a significant step toward GPS-independent navigation for AVs and can extend to domains such as aerial drones, offering pathways to more reliable autonomous systems. Future work will address outdoor validation and scalability.