Search Results (3)

Crop yield estimation plays a crucial role in agricultural production planning and risk management. Utilizing simultaneous localization and mapping (SLAM) technology for the three-dimensional reconstruction of crops allows for an intuitive understanding of their growth status and facilitates yield estimation. Therefore, this paper proposes a VINS-RGBD system incorporating a semantic segmentation module to enrich the information representation of a 3D reconstruction map. Additionally, image matching using L_SuperPoint feature points is employed to achieve higher localization accuracy and obtain better map quality. Moreover, Voxblox is proposed for storing and representing the maps, which facilitates the storage of large-scale maps. Furthermore, yield estimation is conducted using conditional filtering and RANSAC spherical fitting. The results show that the proposed system achieves an average relative error of 10.87% in yield estimation. The semantic segmentation accuracy of the system reaches 73.2% mIoU, and it can save an average of 96.91% memory for point cloud map storage. Localization accuracy tests on public datasets demonstrate that, compared to Shi–Tomasi corner points, using L_SuperPoint feature points reduces the average ATE by 1.933 and the average RPE by 0.042. Through field experiments and evaluations in a strawberry field, the proposed system demonstrates reliability in yield estimation, providing guidance and support for agricultural production planning and risk management. Full article

Perception of the indoor environment is the basis of mobile robot localization, navigation, and path planning, and it is particularly important to construct semantic maps in real time using minimal resources. The existing methods are too dependent on the graphics processing unit (GPU) for acquiring semantic information about the indoor environment, and cannot build the semantic map in real time on the central processing unit (CPU). To address the above problems, this paper proposes a non-use GPU for lightweight indoor semantic map construction algorithm, named NGLSFusion. In the VO method, ORB features are used for the initialization of the first frame, new keyframes are created by optical flow method, and feature points are extracted by direct method, which speeds up the tracking speed. In the semantic map construction method, a pretrained model of the lightweight network LinkNet is optimized to provide semantic information in real time on devices with limited computing power, and a semantic point cloud is fused using OctoMap and Voxblox. Experimental results show that the algorithm in this paper ensures the accuracy of camera pose while speeding up the tracking speed, and obtains a reconstructed semantic map with complete structure without using GPU. Full article

Unmanned Aerial Vehicles (UAVs) require the ability to robustly perceive surrounding scenes for autonomous navigation. The semantic reconstruction of the scene is a truly functional understanding of the environment. However, high-performance computing is generally not available on most UAVs, so a lightweight real-time semantic reconstruction method is necessary. Existing methods rely on GPU, and it is difficult to achieve real-time semantic reconstruction on CPU. To solve the problem, an indoor dense semantic Simultaneous Localization and Mapping (SLAM) method using CPU computing is proposed in this paper, named CDSFusion. The CDSFusion is the first system integrating RGBD-based Visual-Inertial Odometry (VIO), semantic segmentation and 3D reconstruction in real-time on a CPU. In our VIO method, the depth information is introduced to improve the accuracy of pose estimation, and FAST features are used for faster tracking. In our semantic reconstruction method, the PSPNet (Pyramid Scene Parsing Network) pre-trained model is optimized to provide the semantic information in real-time on the CPU, and the semantic point clouds are fused using Voxblox. The experimental results demonstrate that camera tracking is accelerated without loss of accuracy in our VIO, and a 3D semantic map is reconstructed in real-time, which is comparable to one generated by the GPU-dependent method. Full article