Probabilistic Collision Detection Between Noisy Point Clouds Using Robust Classification

2016 ◽  
pp. 77-94 ◽  
Author(s):  
Jia Pan ◽  
Sachin Chitta ◽  
Dinesh Manocha
Keyword(s):  
Collision Detection ◽  
Point Clouds ◽  
Robust Classification

scholarly journals Efficient point cloud collision detection and analysis in a tunnel environment using kinematic laser scanning and K-D Tree search

2014 ◽  
Vol XL-3 ◽  
pp. 289-295 ◽  
Author(s):  
J. Schauer ◽  
A. Nüchter
Keyword(s):  
Cross Section ◽  
Collision Detection ◽  
Point Cloud ◽  
Laser Scanning ◽  
Safety Margin ◽  
Point Clouds ◽  
3D Point Cloud ◽  
Automated Method ◽  
Sharp Turn ◽  
Pass Through

Measuring the structure gauge of tunnels and other narrow passages has so far been the only way to evaluate whether large vehicles can pass through them. But especially for very long vehicles like train wagons and their cargo, the structure gauge is an insufficient measure because the center part of the vehicle between two bogies will inevitably leave the swept volume of its cross section when moving along any other trajectory than a straight line perpendicular to its cross section. In addition, the vehicle as well as the cargo must keep a minimum safety margin from the environment at all points of its trajectory. This paper explores an automated method to check for possible collisions of a model represented by a 3D point cloud moving through the 3D point cloud of an environment. We were given environment data of a train track through a narrow tunnel where simply relying on the structure gauge would indicate that a given wagon would pass through without any collision even though in reality, the train wagon would collide with the inner tunnel wall inside a sharp turn of the tracks. The k-d tree based collision detection method presented in this paper is able to correctly highlight these collisions and indicate the penetration depth of each colliding point of the environment into the model of the train wagon. It can be generalized for any setup where two static point clouds have to be tested for intersection along a trajectory.

scholarly journals ROBUST CLASSIFICATION AND SEGMENTATION OF PLANAR AND LINEAR FEATURES FOR CONSTRUCTION SITE PROGRESS MONITORING AND STRUCTURAL DIMENSION COMPLIANCE CONTROL

2015 ◽  
Vol II-3/W5 ◽  
pp. 129-136 ◽  
Author(s):  
R. Maalek ◽  
D. D. Lichti ◽  
J. Ruwanpura
Keyword(s):  
Moving Objects ◽  
Progress Monitoring ◽  
Point Clouds ◽  
Construction Site ◽  
Structural Dimension ◽  
Construction Sites ◽  
Linear Features ◽  
Robust Clustering ◽  
Robust Classification ◽  
Building Information

The application of terrestrial laser scanners (TLSs) on construction sites for automating construction progress monitoring and controlling structural dimension compliance is growing markedly. However, current research in construction management relies on the planned building information model (BIM) to assign the accumulated point clouds to their corresponding structural elements, which may not be reliable in cases where the dimensions of the as-built structure differ from those of the planned model and/or the planned model is not available with sufficient detail. In addition outliers exist in construction site datasets due to data artefacts caused by moving objects, occlusions and dust. In order to overcome the aforementioned limitations, a novel method for robust classification and segmentation of planar and linear features is proposed to reduce the effects of outliers present in the LiDAR data collected from construction sites. First, coplanar and collinear points are classified through a robust principal components analysis procedure. The classified points are then grouped using a robust clustering method. A method is also proposed to robustly extract the points belonging to the flat-slab floors and/or ceilings without performing the aforementioned stages in order to preserve computational efficiency. The applicability of the proposed method is investigated in two scenarios, namely, a laboratory with 30 million points and an actual construction site with over 150 million points. The results obtained by the two experiments validate the suitability of the proposed method for robust segmentation of planar and linear features in contaminated datasets, such as those collected from construction sites.

scholarly journals Collision Avoidance of Hexacopter UAV Based on LiDAR Data in Dynamic Environment

2020 ◽  
Vol 12 (6) ◽  
pp. 975 ◽  
Author(s):  
Jongho Park ◽  
Namhoon Cho
Keyword(s):  
Collision Avoidance ◽  
Collision Detection ◽  
Three Dimensional ◽  
Dynamic Environment ◽  
Point Clouds ◽  
Lidar Data ◽  
Lidar System ◽  
State Estimate ◽  
Aerial Vehicle ◽  
Moving Obstacle

A reactive three-dimensional maneuver strategy for a multirotor Unmanned Aerial Vehicle (UAV) is proposed based on the collision cone approach to avoid potential collision with a single moving obstacle detected by an onboard sensor. A Light Detection And Ranging (LiDAR) system is assumed to be mounted on a hexacopter to obtain the obstacle information from the collected point clouds. The collision cone approach is enhanced to appropriately deal with the moving obstacle with the help of a Kalman filter. The filter estimates the position, velocity, and acceleration of the obstacle by using the LiDAR data as the associated measurement. The obstacle state estimate is utilized to predict the future trajectories of the moving obstacle. The collision detection and obstacle avoidance maneuver decisions are made considering the predicted trajectory of the obstacle. Numerical simulations, including a Monte Carlo campaign, are conducted to verify the performance of the proposed collision avoidance algorithm.

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