Human Trajectory Prediction Algorithm

Navigating safely within buildings is crucial for mobile robots, especially in environments shared with humans. To address this, we propose a novel method that leverages LiDAR sensor data to identify and predict human movement patterns. Our approach integrates spatial data analysis with a Kalman filter to enhance prediction accuracy. Comprehensive experimental results demonstrate the method's effectiveness, indicating its potential to improve mobile robot navigation in indoor environments. This innovation paves the way for more harmonious and efficient human-robot interaction in shared indoor spaces.