Mobile mapping with ubiquitous point clouds

The 9th International Symposium on Mobile Mapping Technology (MMT 2015) was successfully held in Sydney, Australia on 9−11 December, 2015. MMT2015 attracted about 300 registered participants from 3...

have a wide impact in diverse applications, ranging from detailed mapping of (large) cultural heritage sites to wide area mapping of street corridors for autonomous driving.

Papers in the special issue
This special issue selects 7 submissions from 156 full papers to provide an insight into the latest developments and trends in point cloud processing for mobile mapping and related applications.
Registration between scans and imagery is receiving more and more attention. The paper of Yoshimura et al. (2016) investigates a method to register the point clouds of vehicle-based mobile laser scanning and SFM meshes from aerial photographs. In this paper, 2D feature points extracted from SFM meshes and MLS point cloud are regarded as primitives. The planes of ground and buildings are extracted by region growing, RANSAC and least square method firstly, and intersect these planes to extract vertical edges. Then 2D feature points are required by the intersection between these vertical edges and the ground plane. Finally, scaling ICP algorithm (SICP) is applied for fine registration. Experiments show the effectiveness of the registration method for urban areas. Kang (2016) propose an optimized BaySAC method for processing indoor point clouds in terms of high computing efficiencies and robustness. The proposed method adopts Bayesian Sampling Consensus (BaySAC) method which combines the Bayesian theory and RANSAC for processing indoor point clouds. Moreover, the advantages of the proposed method are verified with the registration of point clouds and fitting of planar features of point clouds, demonstrating that the optimized BaySAC method achieves better performances in terms of accuracies and computing efficiencies than those of RANSAC. The paper by Sun et al. (2016) explores the precise GNSS positioning for UAV based photogrammetry mapping that integrates differential GNSS positioning and GNSS-supported aero-triangulation. Compared with GPS positioning, the positioning accuracy and robustness are improved. This study may provide a feasible and cheap solution for collecting imagery from a wide area with considerable accuracies and a few ground control points.
Fusing data from mobile mapping sensors is of vital importance, as that is the precondition of capturing correct data (e.g. point clouds). Hollick, Helmholz, and Belton (2016) studied the fusion of data from digital camera, accelerometer and gyroscope sensors, and proposed an automated method to construct a graph to describe the relations between sensors. The preliminary results show that the proposed method has potential to fuse different types of sensors dynamically. On the other hand, collecting indoor data with portable sensors is becoming more and   Kalantari and Nechifor (2016) investigated the accuracies of structure sensors for collecting indoor data with suggested guidelines. The related experimental results show that the structure sensor can be adopted to collect indoor data of small areas (e.g. a small room) for 3D indoor mapping. The paper by Masiero et al. (2016) explores the use of smartphones for mobile mapping and presents a new strategy to improve feature matching of imagery captured by smartphones for indoor navigation. The proposed solution integrates indoor positioning and 3D indoor reconstruction, aiming to provide an indoor navigation solution running on standard mobile devices. Panoramic imagery is also captured by mobile platforms and is widely used for street scene maps. The paper of Ghouaiel and Léfèvre (2016) presents a method to couple ground-level panoramas and aerial imagery for landscape change detection. The presented method adopts an inverse perspective mapping solution to transform the panoramic photos onto a top-down aerial view. Then, registration between the panoramic imagery and the aerial imagery can be fulfilled and the change detections are further explored. These interesting studies may provide a cheap and rapid solution to check landscape change. Nevertheless, the precision of change detection of the presented method is still an issue to be studied.

Bisheng Yang
In summary, this selection of papers sheds a spotlight onto the field of point cloud processing for mobile map-