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© 2016 IEEE. In this letter, we present a novel global structure from motion (SfM) pipeline that is particularly effective in dealing with low-parallax scenes and camera motion collinear with the features that represent the environment structure. It is therefore particularly suitable in Urban SLAM, in which frequent road-facing motion poses many challenges to conventional SLAM algorithms. Our pipeline includes a recently explored bundle adjustment (BA) method that exploits a feature parameterization using Parallax angle between on-Manifold observation rays (PMBA). It is demonstrated that this BA stage has a consistently stable optimization configuration for features with any parallax and therefore low-parallax features can stay in reconstruction without pre-filtering. To allow practical usage of PMBA, we provide a compatible initialization stage in the SfM to initialize all camera poses simultaneously, exhibiting friendliness to collinear motion. This is achieved by simplifying PMBA into a hybrid graph problem of high connectivity yet small node set size, solved using a robust linear programming technique. Using simulations and a series of publicly available real datasets including "KITTI" and "Bundle Adjustment in the Large," we demonstrate the robustness of the position initialization stage in handling collinear motion and outlier matches, superior convergence performance of the BA stage in the presence of low-parallax features, and effectiveness of our pipeline to handle many sequential or out-of-order urban scenes.

The SPIR, Submersible Pylon Inspection Robot, is developed to provide an innovative and practical solution to keep workers safe during maintenance of underwater structures in shallow waters, which involves working in dangerous water currents, and high-pressure water-jet cleaning. More advanced than work-class Remotely Operated Vehicles technology, the SPIR is automated and required minimum involvement of humans into the working process, thus effectively lowered the learning curve required to conduct work. To make SPIR operate effectively in poor visibility and highly disturbed environments, the multiple new technologies are developed and implemented into the system, including SBL-SONAR-based navigation, 6-DOF stabilisation, and vision-based 3D mapping. Extensive testing and field trials in various bridges are conducted to verify the robotic system. The results demonstrate the suitability of the SPIR in substituting humans for underwater hazardous tasks such as autonomous cleaning and inspection of bridge and wharf piles

In this paper we present a novel extension to the parallax feature based bundle adjustment (BA). We take parallax BA into a manifold form (PMBA) along with an observation-ray based objective function. This formulation faithfully mimics the projective nature in a camera's image formation, resulting in a stable optimization configuration robust to low-parallax features. Hence it allows use of fast Dogleg optimization algorithm, instead of the usual Levenberg Marquardt. This is particularly useful in urban SLAM in which diverse outdoor environments and collinear motion modes are prevalent. Capitalizing on these properties, we propose a global initialization scheme in which PMBA is simplified into a pose-graph problem. We show that near-optimal solution can be achieved under low-noise conditions. With simulation and a series of challenging publicly available real datasets, we demonstrate PMBA's superior convergence performance in comparison to other BA methods. We also demonstrate, with the "Bundle Adjustment in the Large" datasets, that our global initialization process successfully bootstrap the full BA in mapping many sequential or out-of-order urban scenes.

© 2013 IEEE. In this paper, we present a novel navigation framework for the Fetch robot in a large-scale environment based on submapping techniques. This indoor navigation system is divided into a submap mapping part and an on-line localization part. For the mapping part, in order to deal with large environments or multi-story buildings, a submap mapping framework fusing two-dimensional (2D) laser scan and 3D point cloud from RGBD sensor is proposed using Google Cartographer. Meanwhile, several image datasets with corresponding poses are created from the RGBD sensor. Thanks to the submap framework, the error is limited corresponding to the size of the map, thus localization accuracy will be improved. For the on-line localization, so as to switch the submaps, the on-line images from the RGBD sensor are used to match the database images using DeepLCD, a deep learning based library for loop closure. Based on the information from DeepLCD and odometry, adaptive Monte Carlo localization (AMCL) is reinitialized to finish the localization task. In order to validate the result accuracy, reflectors and a motion capture system are used to compute the absolute trajectory error (ATE) and the relative pose error (RPE) based on the Gaussian-Newton (GN) algorithm. Finally, the proposed framework is tested on the Fetch simulator and the real Fetch robot, including both submap mapping and on-line localization.

© 2019, Springer Nature Switzerland AG. In this paper, we present a navigation system, including off-line mapping and on-line localization, for the Fetch robot in an indoor environment using Cartographer. This framework aims to build a practical, robust, and accurate Robot Operating System (ROS) package for the Fetch robot. Firstly, using Cartographer and the fusion of data from a laser scan and RGB-D camera, a two-dimensional (2D) off-line map is built. Then, the Adaptive Monte Carlo Localization (AMCL) ROS package is used to perform on-line localization. We use a simulation to validate this method of mapping and localization, then demonstrate our method live on the Fetch robot. A video about the simulation and experiment is shown in https://youtu.be/oOvxTOowe34.