A Small and Lightweight Autonomous Laser Mapping System without GPS

Abstract

Autonomous mapping systems execute multiple tasks that include navigation, location, and map generation via the collaborative work of multiple sensors. They are the object of a substantial research focus in the fields of robotics and remote sensing. Although the state-of-the-art mobile mapping systems typically found in readymade vehicles or robots are reliable, they are rather large and heavy, their cost is high, and they generally use GPS and an inertial measurement unit to position, so their working environments are limited. After reviewing the current state of autonomous mapping systems, we describe the design and development of a small and lightweight autonomous mapping system (ASQ-6DMapSys) without GPS, which incorporates low-cost sensors and components. We describe the layout and selection strategy for sensors and other components in detail, and we present the design methodology for each subsystem. The ASQ-6DMapSys employs a two-dimensional (2D) lidar, an inclinometer, and two wheel encoders, which constitute a pose subsystem that uses extended Kalman filtering and simultaneous localization and mapping techniques to compute the pose of the vehicle body. A lowcost 3D lidar that we developed is also installed on the vehicle body, and the resultant data are aligned with the corresponding pose data of the vehicle body to build a 3D point cloud that describes the global geometry of the environment. We designed and developed every subsystem of the ASQ-6DMapSys, including the robot vehicle, so it will be easy to expand its functions in the future. The ASQ-6DMapSys performs well in indoor, outdoor, and tunnel environments, and the experiments in different environments show that the ASQ-6DMapSys is an effective, small, and lightweight autonomous mapping system with a high performance/price ratio. C 2013 Wiley Periodicals, Inc.

Continuous-Time Laser Frames Associating and Mapping via Multilayer Optimization

“…This study only discusses the problem of continuous-time laser frames associating and mapping without other auxiliary sensors, so it is not authors’ intention to discuss IMU and other components of the ASQWolf system in this paper. More information of the system can be found in ref [ 21 ].…”

“…This study only discusses the problem of continuoustime laser frames associating and mapping without other auxiliary sensors, so it is not authors' intention to discuss IMU and other components of the ASQWolf system in this paper. More information of the system can be found in ref [21]. Each laser frame acquired by the Velodyne VLP-16 scanner can be arranged according to 1800 horizontal points and 16 vertical points, which are described in the spherical coordinate system (as shown in Figure 1).…”

Autonomous obstacle avoidance of an unmanned surface vehicle based on cooperative manoeuvring

“…where f ( x ) = 0 represents the free grids, and f ( x ) = 1 denotes obstacle grids (Hu et al , 2013). The grid should be suitable for the size of the USV because it is easy to calculate the position of USV on the map.…”

Automatic Processing Method for Deformation Monitoring of Circle Tunnels Based on 3D LiDAR Data

“…Full automatic deformation monitoring technology based on LiDAR can monitor the structure in a non-contact way and is expected to be one of the most important directions in the field of SHM (structure health monitoring) in the future [1] [2][3] [4][5] [6]. LiDAR techniques can measure several million points in a short time with a high accuracy and has been used by many researchers for generating three-dimensional models in real-estate industry for visualization, in building renovation projects for surveying, and monitoring structure health [7] [17].…”