Performance of Different SLAM Algorithms for Indoor and Outdoor Mapping Applications

Indoor and outdoor mapping studies can be completed relatively quickly, depending on the developments in Mobile Mapping Systems. Especially in indoor environments where high accuracy GNSS positions cannot be used, mapping studies can be carried out with SLAM algorithms. Although there are many different SLAM algorithms in the literature, each can produce results with different accuracy according to the mapped environment. In this study, 3D maps were produced with LOAM, A-LOAM, and HDL Graph SLAM algorithms in different environments such as long corridors, staircases, and outdoor environments, and the accuracies of the maps produced with different algorithms were compared. For this purpose, a mobile mapping platform using Velodyne VLP-16 LIDAR sensor was developed, and the odometer drift, which causes loss of accuracy in the data collected, was minimized by loop closure and plane detection methods. As a result of the tests, it was determined that the results of the LOAM algorithm were not as accurate as those of the A-LOAM and HDL Graph SLAM algorithms. Both indoor and outdoor environments and the A-LOAM results’ accuracy were two times better than HDL Graph SLAM results.

A Vehicle-Borne Mobile Mapping System Based Framework for Semantic Segmentation and Modeling on Overhead Catenary System Using Deep Learning

Overhead catenary system (OCS) automatic detection is of important significance for the safe operation and maintenance of electrified railways. The vehicle-borne mobile mapping system (VMMS) may significantly improve the data acquisition. This paper proposes a VMMS-based framework to realize the automatic detection and modelling of OCS. The proposed framework performed semantic segmentation, model reconstruction and geometric parameters detection based on LiDAR point cloud using VMMS. Firstly, an enhanced VMMS is designed for accurate data generation. Secondly, an automatic searching method based on a two-level stereo frame is designed to filter the irrelevant non-OCS point cloud. Then, a deep learning network based on multi-scale feature fusion and an attention mechanism (MFF_A) is trained for semantic segmentation on a catenary facility. Finally, the 3D modelling is performed based on the OCS segmentation result, and geometric parameters are then extracted. The experimental case study was conducted on a 100 km high-speed railway in Guangxi, China. The experimental results show that the proposed framework has a better accuracy of 96.37%, outperforming other state-of-art methods for segmentation. Compared with traditional manual laser measurement, the proposed framework can achieve a trustable accuracy within 10 mm for OCS geometric parameter detection.

Automatic Registration of Mobile Mapping System Lidar Points and Panoramic-Image Sequences by Relative Orientation Model

To register mobile mapping system (MMS) lidar points and panoramic-image sequences, a relative orientation model of panoramic images (PROM) is proposed. The PROM is suitable for cases in which attitude or orientation parameters are unknown in the panoramic-image sequence. First, feature points are extracted and matched from panoramic-image pairs using the SURF algorithm. Second, these matched feature points are used to solve the relative attitude parameters in the PROM. Then, combining the PROM with the absolute position and attitude parameters of the initial panoramic image, the MMS lidar points and panoramic-image sequence are registered. Finally, the registration accuracy of the PROM method is assessed using corresponding points manually selected from the MMSlidar points and panoramic-image sequence. The results show that three types of MMSdata sources are registered accurately based on the proposed registration method. Our method transforms the registration of panoramic images and lidar points into image feature-point matching, which is suitable for diverse road scenes compared with existing methods.

Census Mapping in India and Role of GIS: A Look Ahead at Census 2021

The 2021 Census of India for over 1.3 billion population deploying 3 million enumerators, has significant evidence value for 71 countries where census is scheduled during 2021. Census mapping plays a major role in accurate, complete and timely census. It delineates the exact and correct boundaries of all the administrative units. The Indian census has been using Geographic Information System (GIS) technologies over the last three censuses. In this study, we focus on the applications and methodologies being adopted for the census mapping in Census 2021 in India which is going to be the first digital Census of India. Five mobile apps have been developed for data collection and for map-related work. The 2021 Indian census utilises the latest census mapping techniques, namely standardisation of GIS spatial database design, geo-referencing of administrative units and latest mobile mapping application (Arc GIS Quick Capture) for field operations and built-up area digitisation work. We also discuss the various challenges and their solutions for census mapping in India, most prominently a high quality, updated, comprehensive and geo-referenced address registry for accurate data collection and mapping, and the use of geo-referenced high-resolution satellite images at village level for covering the gaps in rural boundary maps.