Embedded Perception and Control System for an Autonomous Surface Vehicle using Visual-LiDAR Fusion and SLAM

Engineering notes

Key topics: autonomous driving, object detection, lidar, perception, control. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

The paper presents the design and implementation of a modular perception and control system for an autonomous surface vehicle (ASV), developed on a System-on-Module (SoM) embedded platform. The system was created with the goal of participating in the international RoboBoat competition. The high-level architecture integrates data from a stereo camera, LiDAR sensor, and a GNSS unit with an Inertial Measurement Unit (IMU), enabling object detection, simultaneous localization and mapping (SLAM), and autonomous navigation. We evaluated the use of a quantized convolutional neural network (YOLOv11) for object classification, obstacle detection based on LiDAR data, and control module operation using NAV2 within the ROS 2 environment under limited computational resources. Accelerating visual detection algorithms with CUDA and TensorRT libraries on the onboard NVIDIA Jetson Orin Nano platform enabled effective real-time and low-energy classification. Tests carried out in simulation demonstrated the correct operation of the perception and control system. The average runtime of the system using the quantised YOLO model with GPU was 26.01 ms. This equates to an approximate speed increase of 6.7 times compared to inference performed on an ARM processor, and an approximate speed increase of 5.5 times compared to a standard CPU.

Links and sources

• Official / arXiv page

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