An Embedded Real-Time System for Human Trajectory Prediction Using ROS 2

초록

Trajectory prediction for humans is essential for autonomously moving agents such as robots. However, prior research has mainly been developed using top-down datasets, whereas real-world drones, mobile robots, and humanoids typically mount cameras facing forward or slightly downward rather than toward the floor. As a result, a viewpoint mismatch makes it difficult to directly apply existing trajectory prediction models. Therefore, aiming at a real-time system operating on embedded hardware while accounting for such camera viewpoints, we designed a ROS2-based integrated system that predicts human trajectories from camera inputs. ROS2 is a robot software framework, and we adopted it because its modular architecture facilitates building real-time pipelines. The proposed system was designed for the NVIDIA Jetson Orin Nano, one of NVIDIA's embedded computing boards. The system detects people from RGB images captured by an Intel RealSense D455 camera using YOLOv8, maintains consistent object IDs across frames via ByteTrack, and accumulates the observed trajectories over time. These observed trajectories are then preprocessed and used as inputs to a pretrained trajectory prediction model, which predicts the future motion trajectories of humans. We validated the proposed system in real time in a corridor environment and confirmed that the integrated processing stream - from camera input to detection, tracking, and prediction - runs smoothly on the Jetson Orin Nano, and that the fine-tuned model generates future trajectories that align with human movement patterns. © 2026 IEEE.

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