A Method for Generating Environmental Maps Based on the Improved YOLOv8

To improve environmental mapping accuracy in complex workshop environments with poor lighting and high ground reflectivity, this paper proposed enhancements to the traditional grid map generation algorithm using instance segmentation. A simulation environment was created to collect data, and several instance segmentation algorithms were compared, with YOLOv8 chosen as the best. Due to the slow inference speed and poor real-time performance on industrial computers, lightweight modifications were made to YOLOv8. These included enhancing the backbone network with the Rep Ghost module, introducing the MPDIoU loss function to improve accuracy without increasing computational load. Three improvement methods were tested, showing that the enhanced YOLOv8 algorithm reduced parameters by 10%, increased inference speed by 19%, and achieved 100% accuracy in grid map generation. This improved algorithm meets the needs for accuracy and speed on resource-limited industrial computers.