3-D site mapping with the CMU autonomous helicopter (original) (raw)

Laser Scanning Airborne Systems -a New Step in Engineering Surveying

The new laser scanning airborne technologies can be successfully used in engineering surveying and geodesy. They are mounted on light airplanes or on helicopters, have a fly speed comprised between 70 to 100 km/h; a height fly comprised between 50 to 100 m and can collect three-dimensional points with a density of 20 to 30 per meter. The advantages of these systems are: reduction of field work time, easy and safe access to objects (railway, roads), permit to create 3D homogeneous spatial data base, ensure the topographic and geodetic support for specific information systems.

3D Laser Scaning for Surveying Aplication

2013

3D Laser Scanning, also known as terrestrial LIDAR, has been commercially available for several years, providing a detailed, reliable, and accurate solution to many surveying and measurement problems, and has become well adopted for plant and facilities applications where accurate three-dimensional detail of complex facilities is critical for efficient design and construction projects. Terrestrial laser scanners deliver a dense point-wise sampling of an object’s surface. For many applications a surface-like reconstruction is required. The most typical example is the visualization of the scanned data. In many respects, laser scanning follows the same general surveying process as other instruments: data is collected in the field, adjusted to the appropriate coordinate system, and relevant features can be extracted to produce deliverables ranging from topographic maps, coordinate values, 2D or 3D CAD drawings etc. This paper describes typical scanning project from field-to-finish, incl...

Topographic Description Potential of Laser UAVs by the Side of Terrestrial Laser Scanning

Airborne Laser Scanning (ALS) is one of the most actual remote sensing technologies and accepted as an alternative technology to the traditional photogrammetry by the advantage of providing dense, precise and low-cost point clouds and accurate three dimensional digital surface and terrain models. However, the technique could not be utilized sufficiently in underdeveloped or developing Countries due to the high cost of required equipment consists of Global Navigation Satellite Systems (GNSS) and Inertial Measurement Unit (IMU) mounted aircrafts, airborne laser scanner, and high capacity work stations. To provide the utilization of ALS technique in underdeveloped and developing Countries which have limited finance, we produced a low cost airborne laser scanner skilled advanced unmanned air vehicle (UAV) in the scope of a National Research and Development Project. The device is able to provide very high resolution point clouds with X, Y planimetric coordinates and altitude Z utilizing real time kinematic positioning. In the paper, the properties of the novel device, achieved precise point clouds and a digital surface model (DSM) are presented. The visual and statistical qualities of the generated DSM were assessed by model-to-model based comparison approach utilizing terrestrial laser scanning (TLS) data in a study area which includes open, forest, and built-up land forms. The results demonstrated the high potential of produced ALS UAV.

Towards Model-Free SLAM Using a Single Laser Range Scanner for Helicopter MAV

cs.technion.ac.il

A new solution for the SLAM problem is presented which makes use of a scan matching algorithm, and does not rely on bayesian filters. The virtual map is represented in the form of an occupancy grid, which stores laser scans based on the estimated position. The occupancy grid is scanned by means of ray casting to get a scan of the virtual world, called "virtual scan". The virtual scan therefore contains data from all previously acquired laser measurements and hence serves as the best representation of the surroundings. New laser scans are matched against the virtual scan to get an estimate of the new position. The scan matching cost function is minimized via an adaptive direct search with boundary updating until convergence. The resulting method is model-free and can be applied to various platforms, including micro aerial vehicles that lack dynamic models. Experimental validation of the SLAM method is presented by mapping a typical office hallway environment with a closed loop, using a manually driven platform and a laser range scanner. The mapping results are highly accurate and the loop closure area appears to be seamless, in spite of no loop closure algorithms and no post-mapping correction processes.

Laser Range Finder in Quadcopter for Autonomous Indoor Mapping

12th Global Engineering, Science and Technology conference, 2016

Recently there has been an increased interest in the development of autonomous flying vehicles like Quadcopter. The outdoor Quadcopters cannot be used in indoor as GPS system does not work well there. Ground robots use laser range finder for mapping and surveillance. Using the laser range finder and inertial measurement unit, an outdoor Quadcopter can be used for indoor surveillance and mapping. In the report it is discussed the use of laser range finder sensor in Quadcopter for indoor use.

Registration of Non-Uniform Density 3D Laser Scans for Mapping with Micro Aerial Vehicles

Micro aerial vehicles (MAVs) pose specific constraints on onboard sensing, mainly limited payload and limited processing power. For accurate 3D mapping even in GPS-denied environments, we have designed a lightweight 3D laser scanner specifically for the application on MAVs. Similar to other custom-built 3D laser scanners composed of a rotating 2D laser range finder, it exhibits different point densities within and between individual scan lines. When rotated fast, such non-uniform point densities influence neighborhood searches which in turn may negatively affect local feature estimation and scan registration. We present a complete pipeline for 3D mapping including pair-wise registration and global alignment of such non-uniform density 3D point clouds acquired in-flight. For registration, we extend a state-of-the-art registration algorithm to include topological information from approximate surface reconstructions. For global alignment, we use a graph-based approach making use of the same error metric and iteratively refine the complete vehicle trajectory. In experiments, we show that our approach can compensate for the effects caused by different point densities up to very low angular resolutions and that we can build accurate and consistent 3D maps in-flight with a micro aerial vehicle.

Construction of digital surface model by multi-sensor integration from an unmanned helicopter

2004

Three dimensional data is in great demand for the various applications. In order to represent 3D space in details, it is indispensable to acquire 3D shape and texture together efficiently. However, there still lack a reliable, quick, cheap and handy method of acquiring three dimensional data of objects at higher resolution and accuracy in outdoor and moving environments. In this research, we propose a combination of a digital camera and a small (cheap) laser scanner with inexpensive IMU and GPS for an unmanned helicopter. Direct geo-referencing is achieved automatically using all the sensors without any ground control points. After the accurate trajectory of the platform with attitude changes are determined through the direct geo-referencing, 3D shape of objects is determined by laser scanner as 3D point cloud data, while texture is acquired by CCD sensor from the same platform simultaneously. A method of direct geo-referencing of range data and CCD images by integrating multi sensors for constructing digital surface model are focused. While measuring, an unmanned helicopter is continuously changing its position and attitude with respect to time. For direct geo-referencing, IMU measures the movement of the platform. IMU has a rising quality, but it is still affected by systematic errors. Through Kalman filter operation, an optimal estimate of the sensor position and attitude are determined from GPS and IMU. Meanwhile, geo-referencing of CCD image is determined by bundle block adjustment. GPS and IMU allow automatic setting of tie-points and they reduce the number of tie-points and searching time of tie-points by limiting of searching area. The result of bundle block adjustment aids Kalman filter. IMU are initialized for Kalman filter using the result of bundle block adjustment. That is, after every bundle block adjustment, IMU and its error are complemented. Geo-referencing of laser range data is done by using the result of aiding Kalman filter. Therefore, geo-referencing of range data and CCD images is done directly to overlap exactly with high accuracy and high resolution. The method of data acquisition and digital surface modelling is developed by direct geo-referencing of laser range data and CCD images with GPS and IMU. This is the way of rendering objects with rich shape and detailed texture automatically. A new method of direct geo-referencing by the combination of bundle block adjustment and Kalman filter is proposed.

Integration Between Unmanned Aerial Vehicle and Terrestrial Laser Scanner in Producing 3D Model

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019

Unmanned Aerial Vehicles (UAV) frequently used for obtaining 2D or 3D data acquisition. Meanwhile, Terrestrial Laser Scanners (TLS) are used for obtaining only 3D data acquisition. However if both are integrated, they were able to produce a more accurate data. The purpose of this study is to investigate the possible integration of point clouds obtained by TLS with UAV images at T06 FBES building through the aerial survey where the roof is scanned and ground survey which scans the facades" building. Topcon GLS 2000 and DJI Inspire 1 UAV were used to acquire the data at the field. The aerial data and ground data were processed using Pix4D and Scanmaster respectively. The data integration process is done by converting both point clouds into the same coordinate system and then by aligning the same points of both points clouds in Cloud Compare. For verification purposes, dimensional survey was done and there are several distances were taken from the study area to validate the accuracy assessment. The result of residuals between the dimension survey and integration is 0.183 m which is below 1 meter. The result of this study is a 3D model of UTM T06 FBES building based on the point cloud accuracy in cm level. To conclude, the integration between these two methods can be implemented to produce an accurate 3D model.