Development of a ground robot for indoor SLAM using Low‐Cost LiDAR and remote LabVIEW HMI

The simultaneous localization and mapping problem (SLAM) is crucial to autonomous navigation and robot mapping. The main purpose of this thesis is to develop a ground robot that implements SLAM to test the performance of the low‐cost RPLiDAR A1M8 by DFRobot. The HectorSLAM package, available in ROS was used with a Raspberry Pi to implement SLAM and build maps. These maps are sent to a remote desktop via TCP/IP communication to be displayed on a LabVIEW HMI where the user can also control robot. The LabVIEW HMI and the project in its entirety is intended to be as easy to use as possible to the layman, with many processes being automated to make this possible. The quality of the maps created by HectorSLAM and the RPLiDAR were evaluated both qualitatively and quanitatively to determine how useful the low‐cost LiDAR can be for this application. It is hoped that the apparatus developed in this project will be used with drones in the future for 3D mapping

An improved robot for bridge inspection

This paper presents a significant improvement from the previous submission from the same authors at ISARC 2016. The robot is now equipped with low-cost cameras and a 2D laser scanner which is used to monitor and survey a bridge bearing. The robot is capable of localising by combining a data from a pre-surveyed 3D model of the space with real-time data collection in-situ. Autonomous navigation is also performed using the 2D laser scanner in a mapped environment. The Robot Operating System (ROS) framework is used to integrate data collection and communication for navigation

Design of a Robotic Inspection Platform for Structural Health Monitoring

Actively monitoring infrastructure is key to detecting and correcting problems before they become costly. The vast scale of modern infrastructure poses a challenge to monitoring due to insufficient personnel. Certain structures, such as refineries, pose additional challenges and can be expensive, time-consuming, and hazardous to inspect. This thesis outlines the development of an autonomous robot for structural-health-monitoring. The robot is capable of operating autonomously in level indoor environments and can be controlled manually to traverse difficult terrain. Both visual and lidar SLAM, along with a procedural-mapping technique, allow the robot to capture colored-point-clouds. The robot is successfully able to automate the point cloud collection of straightforward environments such as hallways and empty rooms. While it performs well in these situations, its accuracy suffers in complex environments with variable lighting. More work is needed to create a robust system, but the potential time savings and upgrades make the concept promising

The simultaneous localization and mapping (SLAM):An overview

Positioning is a need for many applications related to mapping and navigation either in civilian or military domains. The significant developments in satellite-based techniques, sensors, telecommunications, computer hardware and software, image processing, etc. positively influenced to solve the positioning problem efficiently and instantaneously. Accordingly, the mentioned development empowered the applications and advancement of autonomous navigation. One of the most interesting developed positioning techniques is what is called in robotics as the Simultaneous Localization and Mapping SLAM. The SLAM problem solution has witnessed a quick improvement in the last decades either using active sensors like the RAdio Detection And Ranging (Radar) and Light Detection and Ranging (LiDAR) or passive sensors like cameras. Definitely, positioning and mapping is one of the main tasks for Geomatics engineers, and therefore it's of high importance for them to understand the SLAM topic which is not easy because of the huge documentation and algorithms available and the various SLAM solutions in terms of the mathematical models, complexity, the sensors used, and the type of applications. In this paper, a clear and simplified explanation is introduced about SLAM from a Geomatical viewpoint avoiding going into the complicated algorithmic details behind the presented techniques. In this way, a general overview of SLAM is presented showing the relationship between its different components and stages like the core part of the front-end and back-end and their relation to the SLAM paradigm. Furthermore, we explain the major mathematical techniques of filtering and pose graph optimization either using visual or LiDAR SLAM and introduce a summary of the deep learning efficient contribution to the SLAM problem. Finally, we address examples of some existing practical applications of SLAM in our reality

2D Mapping and boundary detection using 2D LIDAR sensor for prototyping Autonomous PETIS (Programable Vehicle with Integrated Sensor)

PETIS (Programable Vehicle with Integrated Sensor) is a research project with goal make a robot that move independently with specific purpose. Due complexity of PETIS, research divide into several important sequence. In this research author focus on sense of sight for PETIS, LIDAR chosen due flexible and comprehensive. There is many LIDAR sensor in marketplace, LDS-01 as one of commercial LIDAR sensor available on market, produced by ROBOTIS as one of low-cost LIDAR sensor. Compare with another sensor that cost more than $1000, LDS-01 just cost lower than$500. On this research study focus with LDS-01 sensor reading, include hardware, software connection, and data handling. Based on this research LDS-01 as LIDAR sensor can read obstacle with minimum 29,9 cm and maximal 290,7 cm. Comparing with datasheet LDS-01 should work from 12 cm through 350 cm.