Visual Simultaneous Localization and Mapping (VSLAM) plays an important role in advanced driver assistance systems and autonomous driving. Feature-based VSLAM generates very promising and visually pleasant results due to its robustness and localization precision. However, traditional feature-based VSLAM systems are prone to be degraded or fail when either the environment or the motion of robots is too challenging. To handle these problems, we proposed BASL-AD SLAM. Firstly, we leveraged the robustness of deep learning and designed a binary deep learning-based descriptor to enhance the accuracy of feature detection and matching for SLAM systems in challenging environments. Meanwhile, the real-time performance of the SLAM system can be also guaranteed. Furthermore, we proposed an adaptive motion model to supply more accurate initial poses, which facilitated subsequent feature tracking and pose optimization in SLAM. The performance was validated on public datasets. Results verified that our BASL-AD SLAM can carry out robust feature matching and tracking in real-time under challenging environments, meanwhile, pose estimation accuracy was significantly improved and the proposed SLAM system showed competing robustness and accuracy compared with ORB-SLAM3.

Using unmanned aerial vehicles (UAVs) for performing automatic inspection of overhead power lines instead of foot patrols is an attractive option, since doing so is safer and have considerable cost savings, among other advantages. The purpose of this paper is to design a 3D laboratory test-platform to simulate UAVs' inspection of transmission lines and secondly, proposing an automated inspection strategy for UAVs in order to follow transmission lines. The construction and system architecture of our 3D test-platform is described in this paper. The inspection strategy contributes to knowledge pertaining to an automated inspection procedure and includes two steps: flight path planning for UAVs and visual tracking of the transmission lines. The 3D laboratory test-platform is applied to test the performance of the proposed strategy and the tracking results of our inspection strategy are subsequently presented. The availability of the 3D laboratory test-platform and the efficiency of our tracking algorithm are verified by experiments.

Loop closure detection is a significant requirement for simultaneous localization and mapping (SLAM) to recognize revisited place. This paper presents a novel line-based loop closure detection method for vision-based SLAM that allows reliable loop closure detections, especial under structural environment. The performance of coping with perceptual aliasing conditions is more competitive than point based methods. The bag of words model is extended in this work which uses only line features. A variant of TF-IDF (term frequency & inverse document frequency) scoring scheme is proposed by adding a discrimination coefficient to improve the discrimination of image similarity scores, further to reinforce the similarity evaluation of two images. LBD (Line Band Descriptor) and binary LBD features are extracted to build visual vocabularies. Temporal consistency and spatial continuity checks enhance detection reliability. The performance of proposed scoring scheme was compared with original TF-IDF, results show that our proposed scheme has competitive discrimination ability. We also compared the query performance of our vocabularies with ORB-based, MSLD (mean standard-deviation line descriptor)-based, and PL (Point-and-Line)-based vocabularies, results indicate that our vocabularies obtain the highest successful retrieval rate. The performance of the whole loop closure detection algorithm was also evaluated in terms of precision, recall and efficiency, which were compared with ORB, MSLD, PL-based methods, and also with CNN-based method, results demonstrate that our method is superior to others with satisfactory precision and efficiency.

In the process of inspection for the electricity transmission equipment using flying robots, an automatic way to find the best viewpoint to get the high quality of image would be beneficial to observe the state of the electricity transmission equipment. Finding the best viewpoint belongs to the viewpoint selection problem. A strategy is proposed in this paper to find the best viewpoint for the electricity transmission equipment inspection, according to which the candidate viewpoints can be generated based on the 3D model of the equipment. Then a scoring function is constructed which combines three factors including the amount of the information, the viewing quality, and the visibility of the information to evaluate the quality of the candidate viewpoint. Finally, the best viewpoint can be selected from all of candidate viewpoints according to the scoring function. The proposed algorithm is tested on the experiment to demonstrate its effectiveness.

We address the problem of depth estimation from a single monocular image in the paper. Depth estimation from a single image is an ill-posed and inherently ambiguous problem. In the paper, we propose an encoder-decoder structure with the feature pyramid to predict the depth map from a single RGB image. More specifically, the feature pyramid is used to detect objects of different scales in the image. The encoder structure aims to extract the most representative information from the original image through a series of convolution operations and to reduce the resolution of the input image. We adopt Res2-50 as the encoder to extract important features. The decoder section uses a novel upsampling structure to improve the output resolution. Then, we also propose a novel loss function that adds gradient loss and surface normal loss to the depth loss, which can predict not only the global depth but also the depth of fuzzy edges and small objects. Additionally, we use Adam as our optimization function to optimize our network and speed up convergence. Our extensive experimental evaluation proves the efficiency and effectiveness of the method, which is competitive with previous methods on the Make3D dataset and outperforms state-of-the-art methods on the NYU Depth v2 dataset.