Power transformer is one of the most important equipments for the power distribution system, and it is of crucial significance to guard electricity safety. A small wireless controlled robot was developed for the internal detection of oilimmersed transformer, which improves the automation and intelligent level of oil-immersed transformer fault detection and maintenance. Conventional FastSLAM is known to degenerate over time in terms of accuracy due to the particle depletion in resampling phase. In order to achieve a precise and efficient SLAM for mobile robots in high similarity environments, a Particle Swarm Optimization (PSO) SLAM algorithm is applied to the robot. Simulations verify its effectiveness and feasibility.

After a lengthy period of scouring, the reinforced concrete surface of the dam spillway (i.e., drift spillways and flood discharge spillways) will suffer from deterioration and damage. Regular manual inspection is time-consuming and dangerous. This paper presents a robotic solution to detect automatically, count defect instance numbers, and reconstruct the surface of dam spillways by incorporating the deep learning method with a visual 3D reconstruction method. The lack of a real dam defect dataset and incomplete registration of minor defects on the 3D mesh model in fusion step are two challenges addressed in the paper. We created a multi-class semantic segmentation dataset of 1711 images (with resolutions of 848 × 480 and 1280 × 720 pixels) acquired by a wall-climbing robot, including cracks, erosion, spots, patched areas, and power safety cable. Then, the architecture of the U-net is modified with pixel-adaptive convolution (PAC) and conditional random field (CRF) to segment different scales of defects, trained, validated, and tested using this dataset. The reconstruction and recovery of minor defect instances in the flow surface and sidewall are facilitated using a keyframe back-projection method. By generating an instance adjacency matrix within the class, the intersection over union (IoU) of 3D voxels is calculated to fuse multiple instances. Our segmentation model achieves an average IoU of 60% for five defect class. For the surface model’s semantic recovery and instance statistics, our method achieves accurate statistics of patched area and erosion instances in an environment of 200 m2, and the average absolute error of the number of spots and cracks has reduced from the original 13.5 to 3.5.

For the surface defects inspection task, operators need to check the defect in local detail images by specifying the location, which only the global 3D model reconstruction can’t satisfy. We explore how to address multi-type (original image, semantic image, and depth image) local detail image synthesis and environment data storage by introducing the advanced neural radiance field (Nerf) method. We use a wall-climbing robot to collect surface RGB-D images, generate the 3D global model and its bounding box, and make the bounding box correspond to the Nerf implicit bound. After this, we proposed the Inspection-Nerf model to make Nerf more suitable for our near view and big surface scene. Our model use hash to encode 3D position and two separate branches to render semantic and color images. And combine the two branches’ sigma values as density to render depth images. Experiments show that our model can render high-quality multi-type images at testing viewpoints. The average peak signal-to-noise ratio (PSNR) equals 33.99, and the average depth error in a limited range (2.5 m) equals 0.027 m. Only labeled 2% images of 2568 collected images, our model can generate semantic masks for all images with 0.957 average recall. It can also compensate for the difficulty of manual labeling through multi-frame fusion. Our model size is 388 MB and can synthesize original and depth images of trajectory viewpoints within about 200 m2 dam surface range and extra defect semantic masks.