This paper describes the implementation of a learning-based lane detection algorithm on an Autonomous Mobile Robot. It aims to implement the Ultra Fast Lane Detection algorithm for real-time application on the SEATER P2MC-BRIN prototype using a camera and optimize its performance on the Jetson Nano platform. Preliminary experiments were conducted to evaluate the algorithm's performance in terms of data processing speed and accuracy using two types of datasets: outdoor using a public dataset and indoor using an internal dataset from the indoor area of the BRIN Workshop Building in Bandung. The experiments revealed that the algorithm runs more optimally on the Jetson Nano platform after conversion to TensorRT compared to the ONNX model, achieving processing speeds of approximately 101 ms using CULane and 105 ms using TuSimple, which is about 22 times faster than the previous model. While the algorithm demonstrates good accuracy on the outdoor public dataset, its performance falls short on the indoor dataset. Future work should focus on transfer learning and fine-tuning to enhance indoor lane detection accuracy.

翻译：本文描述了在自主移动机器人上实现基于学习的车道检测算法。研究旨在为SEATER P2MC-BRIN原型机部署Ultra Fast Lane Detection算法，通过摄像头实现实时应用，并在Jetson Nano平台上优化其性能。通过初步实验评估了算法在数据处理速度和精度方面的表现，实验采用两类数据集：使用公开数据集的室外场景，以及使用万隆BRIN Workshop Building室内区域内部数据集的室内场景。实验结果表明，相较于ONNX模型，算法转换为TensorRT后在Jetson Nano平台上运行更优，使用CULane数据集处理速度约为101毫秒，使用TuSimple数据集约为105毫秒，比原模型提升约22倍。虽然算法在室外公开数据集上表现出良好精度，但在室内数据集上性能不足。未来工作应聚焦于迁移学习和微调，以提升室内车道检测的准确度。