Autonomous 3D acquisition of outdoor environments poses special challenges. Different from indoor scenes, where the room space is delineated by clear boundaries and separations (e.g., walls and furniture), an outdoor environment is spacious and unbounded (thinking of a campus). Therefore, unlike for indoor scenes where the scanning effort is mainly devoted to the discovery of boundary surfaces, scanning an open and unbounded area requires actively delimiting the extent of scanning region and dynamically planning a traverse path within that region. Thus, for outdoor scenes, we formulate the planning of an energy-efficient autonomous scanning through a discrete-continuous optimization of robot scanning paths. The discrete optimization computes a topological map, through solving an online traveling sales problem (Online TSP), which determines the scanning goals and paths on-the-fly. The dynamic goals are determined as a collection of visit sites with high reward of visibility-to-unknown. A visit graph is constructed via connecting the visit sites with edges weighted by traversing cost. This topological map evolves as the robot scans via deleting outdated sites that are either visited or become rewardless and inserting newly discovered ones. The continuous part optimizes the traverse paths geometrically between two neighboring visit sites via maximizing the information gain of scanning along the paths. The discrete and continuous processes alternate until the traverse cost of the current graph exceeds the remaining energy capacity of the robot. Our approach is evaluated with both synthetic and field tests, demonstrating its effectiveness and advantages over alternatives.

With the technology enhanced, 3D mobile light detection and ranging (LiDAR) can produce more accurate 3D information for the objects. Meanwhile, hyperspectral remote sensing has more number of wavelengths and provides a higher resolution spectrum of objects. This paper proposes a multi-sensor platform to provide these two data for health detection at the individual tree level in urban environments. We firstly locate and segment the suspected tree objects by ground removal and Euclidean distance clustering. Then we take use of spectrum to remove non-tree objects, e.g., buildings, light poles. After that, we use LiDAR data to compute the geometric parameters of each tree and hyperspectral data to analyze its health situation.

Airborne acquisition and ground-view 3D point cloud provide complementary 3D information at city scale. A complete but lacks ground-view details, while the latter is incomplete for higher floors and severe occlusion. In this paper, First, a volumetric fusion method based on graph cuts were applied for fusing of airborne and terrestrial 3D LiDAR data. Second, we propose a method of constraints based on the local centroid of point cloud to eliminate the gap of fusion boundary. Finally, the experiments show that the improved fusion algorithm implement blending effectively.

This paper presents a new method for extracting urban roadside trees automatically from mobile laser scanning point clouds. This method mainly includes three steps. First, ground point clouds are removed by voxel-based upward growing method. Second, Euclidean distance segment method is used to cluster non-ground point clouds into certain individual objects. Then crown seeds of the initial layer is found by comparing the number of points in each layer after using the voxel modeling algorithm. Crown seeds in other layers can thereafter be detected via the upward inter-sectional analysis. Third, a crown voxel growing algorithm is used to make the crown grow in horizontal. The experimental results show that the voxel models of the individual roadside trees can be automatically and effectively extracted with our method.

For survey and maintenance of traffic signs, this paper presents a novel traffic sign occlusion detection method using 3-D point clouds and trajectory data acquired by a mobile laser scanning system. To produce a maintenance guide, our method aims to obtain the degree of occlusion by analyzing the spatial relationship between traffic signs, surroundings, and drivers on the road. First, a detection method considering both reflectance and geometric features is developed to capture traffic signs. Next, to simulate the driver's view, a trajectory-based method is proposed to determine driver's observation location and the corresponding observed traffic sign. Finally, to determine whether a traffic sign is in occlusion, a hidden point removal algorithm is adopted and carried out. Furthermore, we develop two indices to evaluate the degree of occlusion. The proposed method is tested using two point cloud data sets collected by an RIEGL VMX-450 system along a 23.68-km-long urban road. The obtained results illustrate the feasibility of the proposed occlusion detection method.

LIDAR (Light Detection and Ranging) is an especially effective tool for acquiring geo-referenced point clouds of urban site. Accurate extraction of elevated features such as building rooftops is vitally important in various applications. However, it is still challenging to determine an accurate rooftop contour from the irregularly distributed LIDAR point clouds. In this paper an efficient LIDAR segmentation method is presented in order to achieve automated rooftop extraction. First, we apply a voxel-based upward growing algorithm that filters out the ground points from the raw point cloud scenes. Second, we employ a Euclidean based clustering method on non-ground points by making use of nearest neighbors. Then we introduce RANSAC (RANdom SAmple Consensus) technique to estimate primitive planes for fitting rooftop facets. Finally, we use concave hull and L0regularization to determine the rooftop contour. Accurate experimental results demonstrate the validity of our segmentation method for rooftop extraction.

This paper presents a new algorithm to directly extract 3D road boundaries from mobile laser scanning (MLS) point clouds. The algorithm includes two stages: 1) non-ground point removal by a voxel-based elevation filter, and 2) 3D road surface extraction by curb-line detection based on energy minimization and graph cuts. The proposed algorithm was tested on a dataset acquired by a RIEGL VMX-450 MLS system. The results fully demonstrate the effectiveness and superiority of the proposed algorithm.

This paper proposes a method for automated extraction of street trees in a typical urban environment from 3D point cloud data acquired by the mobile laser scanning system. First, the algorithm utilizes the voxel-based method to remove the ground points from the scene. Second, the Euclidean distance clustering is adopted to cluster points into individual objects. The eigenvalues of neighborhood covariance matrix and the corresponding normalized centroid distance are computed for each point to obtain the subdivided dimensional features. Finally, the statistical component features and horizontal information are calculated for object detection. The experiment results show the feasibility of the proposed algorithm.