Simultaneous Localization and Mapping (SLAM) is a framework to estimate sensor position and point clouds simultaneously in unknown environments. This article first describes data processing flows and open sources for autonomous robots and surveying. Next, this article briefly describes examples of SLAM applications such as a lunar rover and water-borne MMS. Moreover, this article describes the technical issues and future works of the SLAM.

In past years, the price of expensive LiDAR (Light Detection And Ranging) has dropped significantly due to competition in the development of self-driving cars and robots. At the same time, SLAM (Simultaneous Localization And Mapping) technology, which consists of self-position estimation and surrounding environment map generation, has attached. The combination of this device and technology has rapidly progressed as LiDAR SLAM technology because it has high ranging accuracy, can be detected at long distances, and is possible even at night.

In this paper, we will introduce the application fields of LiDAR SLAM technology except robotics and the products used this technology, and finally we will discuss future developments.

The maintenance of 3D data is necessary to record the current state of castle ruins and remains for restoration work due to damage from the natural disaster. In acquiring data on the remains castle ruins of Kajo Park in Yamagata City, it was a challenge to obtain accurate 3D data on the remains without any blind spots and restricting access to the park by visitors. However, previous 3D data measurement methods like airborne laser measurement methods using aircraft and UAVs and ground-mounted laser measurement methods have problems in efficiency and measurement range for 3D data acquisition in small areas. This paper describes a case study of obtaining 3D data with a handheld laser scanner of castle ruins at Kajo Park in Yamagata City.

In recent years, the surveying industry has been using the terrestrial laser scanners more frequently with the promotion of BIM/CIM by the Ministry of Land, Infrastructure, Transport and Tourism. As one of the latest models of laser scanners, a hand-held laser scanner that has a SLAM function and synthesizes point cloud data while moving has appeared. We present three-dimensional modeling of river structures using a hand-held laser scanner BLK2GO.

It is important to improve the efficiency of bridge inspections because of deterioration of many bridges and shortage of engineers. In this paper, for the purpose of efficienct inspection without overlook at large-scale special bridges, we examined the use of 3D models and orthomosaic images generated from images acquired by UAV equipped with Visual SLAM. As a result, it was confirmed that 3D models with accuracy and orthomosaic images which can recognize the defects of bridge are generated and be able to use for bridge inspection and repair.

In Shizuoka Prefecture's “VIRTUAL SHIZUOKA” concept, point cloud data for the entire Shizuoka Prefecture was developed as open data. Among them, the point cloud data was acquired by the measurement equipment using SLAM about the Mt. Fuji climbing route. This document introduces the measurement implementation example.

This study investigates a method for generating road orthoimages using onboard images taken at night by a high-sensitivity digital camera for avoiding the effects of sunlight and human/vehicle congestion. Perspective projection images from onboard cameras are generally transformed into overhead images by projection transformation using feature quantities such as feature points. The mosaicing process is also based on the feature quantities between the overhead images. Therefore, the generation of feature-based night road orthoimages is an ill-posed problem. Log-polar transform (LPT) with rotation and scale invariance and the correlation-based phase-only correlation (POC) method are used for the above problem. However, in order to keep the properties of the LPT, the problem of translation in Log-polar images has to be resolved.

With this motivation, simulated images in which the reference image is moved slightly in the direction of the car's travel sequentially generate and determine the simulated-reference image under the similarity between each simulated image and the target image. Furthermore, the reference image is geometrically corrected by the LPT between the simulated-reference image and the target image, and the generation of night road orthoimages as an ill-posed problem becomes a well-posed problem by the POC on the corrected reference image and the target image is described in this paper.