A Decentralized Sensor Fusion Scheme for Multi Sensorial Fault Resilient Pose Estimation

This article proposes a novel decentralized two-layered and multi-sensorial based fusion architecture for establishing a novel resilient pose estimation scheme. As it will be presented, the first layer of the fusion architecture considers a set of distributed nodes. All the possible combinations of pose information, appearing from different sensors, are integrated to acquire various possibilities of estimated pose obtained by involving multiple extended Kalman filters. Based on the estimated poses, obtained from the first layer, a Fault Resilient Optimal Information Fusion (FR-OIF) paradigm is introduced in the second layer to provide a trusted pose estimation. The second layer incorporates the output of each node (constructed in the first layer) in a weighted linear combination form, while explicitly accounting for the maximum likelihood fusion criterion. Moreover, in the case of inaccurate measurements, the proposed FR-OIF formulation enables a self resiliency by embedding a built-in fault isolation mechanism. Additionally, the FR-OIF scheme is also able to address accurate localization in the presence of sensor failures or erroneous measurements. To demonstrate the effectiveness of the proposed fusion architecture, extensive experimental studies have been conducted with a micro aerial vehicle, equipped with various onboard pose sensors, such as a 3D lidar, a real-sense camera, an ultra wide band node, and an IMU. The efficiency of the proposed novel framework is extensively evaluated through multiple experimental results, while its superiority is also demonstrated through a comparison with the classical multi-sensorial centralized fusion approach.

3D LiDAR Scanning of Urban Forest Structure Using a Consumer Tablet

Forest measurements using conventional methods may not capture all the important information required to properly characterize forest structure. The objective of this study was to develop a low-cost alternative method for forest inventory measurements and characterization of forest structure using handheld LiDAR technology. Three-dimensional (3D) maps of trees were obtained using an iPad Pro with a LiDAR sensor. Freely-available software programs, including 3D Forest Software and CloudCompare software, were used to determine tree diameter at breast height (DBH) and distance between trees. The 3D point cloud data obtained from the iPad Pro LiDAR sensor was able to estimate tree DBH accurately, with a residual error of 2.4 cm in an urban forest stand and 1.9 cm in an actively managed experimental forest stand. Distances between trees also were accurately estimated, with mean residual errors of 0.21 m for urban forest, and 0.38 m for managed forest stand. This study demonstrates that it is possible to use a low-cost consumer tablet with a LiDAR sensor to accurately measure certain forest attributes, which could enable the crowdsourcing of urban and other forest tree DBH and density data because of its integration into existing Apple devices and ease of use.

Obstacle Detection Based on 3D Lidar Euclidean Clustering

Environment perception is the basis of unmanned driving and obstacle detection is an important research area of environment perception technology. In order to quickly and accurately identify the obstacles in the direction of vehicle travel and obtain their location information, combined with the PCL (Point Cloud Library) function module, this paper designed a euclidean distance based Point Cloud clustering obstacle detection algorithm. Environmental information was obtained by 3D lidar, and ROI extraction, voxel filtering sampling, outlier point filtering, ground point cloud segmentation, Euclide clustering and other processing were carried out to achieve a complete PCL based 3D point cloud obstacle detection method. The experimental results show that the vehicle can effectively identify the obstacles in the area and obtain their location information.