No 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.

Aiming at addressing the problem of high computational cost of the traditional Kalman filter in SINS/GPS, a practical optimization algorithm with offline-derivation and parallel processing methods based on the numerical characteristics of the system is presented in this paper. The algorithm exploits the sparseness and/or symmetry of matrices to simplify the computational procedure. Thus plenty of invalid operations can be avoided by offline derivation using a block matrix technique. For enhanced efficiency, a new parallel computational mechanism is established by subdividing and restructuring calculation processes after analyzing the extracted “useful” data. As a result, the algorithm saves about 90% of the CPU processing time and 66% of the memory usage needed in a classical Kalman filter. Meanwhile, the method as a numerical approach needs no precise-loss transformation/approximation of system modules and the accuracy suffers little in comparison with the filter before computational optimization. Furthermore, since no complicated matrix theories are needed, the algorithm can be easily transplanted into other modified filters as a secondary optimization method to achieve further efficiency.

The tree diameter at breast height (DBH) is one of the most important variables for monitoring the forest ecology. Mobile laser scanning (MLS), which has been widely applied in the forestry field, makes DBH measurement fast and convenient. However, there are many shrubs and deadwood in the neutral forest environment and the point clouds quality from MLS are easily affected by the environment which results in low single tree segmentation and DBH estimation accuracy. To improve the accuracy in a complex forest environment and low point cloud quality, we propose a relative density segmentation method for the single tree segmentation and DBH estimation method based on multi-height diameters for the DBH estimation. The relative density segmentation method calculates the relative density according to the ratio of density in two different scales, and segments the tree trunks by the higher relative density of trunk point clouds compared with their surroundings points. In the natural forest plot, the precision and recall of the proposed segmentation method reached 0.966 and 0.946, respectively; In the urban forest plot, the precision and recall reached 1 and 0.966, respectively. The proposed DBH estimation method was used to estimate the DBH of trees using multi-height diameters. The multi-height diameters combined with the outlier detection algorithm were able to improve the accuracy and robustness when the trunk point clouds have large noise. For the DBH estimation results, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were 2.5 cm, 11.54%, and 3.17 cm, respectively, in the natural forest plot and 1.65 cm, 6.31%, and 1.97 cm, respectively, in the urban forest plot. The good experiment results indicate that the proposed method can achieve accurate and robust DBH extraction and provide fundamental data for supervision and sustainable development of forest resources.

Abstract:The complexity of the single linear hyperspectral pushbroom imaging based on a high altitude airship (HAA) without a three-axis stabilized platform is much more than that based on the spaceborne and airborne. Due to the effects of air pressure, temperature and airflow, the large pitch and roll angles tend to appear frequently that create pushbroom images highly characterized with severe geometric distortions. Thus, the in-flight calibration procedure is not appropriate to apply to the single linear pushbroom sensors on HAA having no three-axis stabilized platform. In order to address this problem, a new ground-based boresight calibration method is proposed. Firstly, a coordinate's transformation model is developed for direct georeferencing (DG) of the linear imaging sensor, and then the linear error equation is derived from it by using the Taylor expansion formula. Secondly, the boresight misalignments are worked out by using iterative least squares method with few ground control points (GCPs) and ground-based side-scanning experiments. The proposed method is demonstrated by three sets of experiments: (i) the stability and reliability of the method is verified through simulation-based experiments; (ii) the boresight calibration is performed using ground-based experiments; and (iii) the validation is done by applying on the orthorectification of the real hyperspectral pushbroom images from a HAA Earth observation payload system developed by our research team-"LanTianHao". The test results show that the proposed boresight calibration approach significantly improves the quality of georeferencing by reducing the geometric distortions caused by boresight misalignments to the minimum level.