Autonomous navigation in dynamic environments presents a significant challenge for mobile robotic systems. This paper proposes a novel approach utilizing Convolutional Neural Networks (CNNs) for multi-object detection in 3D space and 2D segmentation using bird’s eye view (BEV) maps derived from 3D Light Detection and Ranging (LiDAR) data. Our method aims to enable mobile robots to localize movable objects and their occupancy, which is crucial for safe and efficient navigation. To address the scarcity of labeled real-world datasets, a synthetic dataset based on a simulation environment is generated to train and evaluate our model. Additionally, we employ a subset of the NVIDIA r2b dataset for evaluation in the real world. Furthermore, we integrate our CNN-based detection and segmentation model into a Robot Operating System 2 (ROS2) framework, facilitating communication between mobile robots and a centralized node for data aggregation and map creation. Our experimental results demonstrate promising performance, showcasing the potential applicability of our approach in future assembly systems. While further validation with real-world data is warranted, our work contributes to advancing perception systems by proposing a solution for multi-source, multi-object tracking and mapping.

The semantic segmentation of log faces constitutes the initial step towards subsequent quality analyses of timber,such as quantifying properties like mechanical strength, durability, and the aesthetic attributes of growth rings. In the literature, works based on both classical and machine learning approaches for this purpose can be found. However, more recent architectures and techniques, such as ViTs or even the latest CNNs, have not yet been thoroughly evaluated. This study presents a comparison of modern deep neural network architectures for cross-section segmentation in images of log ends. The results obtained indicate that the networks using the ViTs considered in this work outperformed those previously evaluated in terms of both accuracy and processing time.