Survey on UAV, lidar and underground mapping
Image source: H2H Associates, DroneZon

Survey on UAV, lidar and underground mapping

This survey presents an overall view of lidar (Light Detection And Ranging) used on UAV (Unmanned Aerial Vehicle) and their project.

Nowadays, the number of projects which are using UAV with lidar are booming. Because of their ability to access areas that human can’t go and their ease of use, UAV are better able to give different interesting viewpoints than UGV (Unmanned Ground Vehicle). However, the use of lidar on UAV is still challenging and a lot of issues should be overcome. The pose of UAV, its speed and altitude, features of equipment and algorithms used significantly influence the accuracy of mapping. In this survey, we present a list of articles using lidar on UAV and give some details about the equipment and algorithms used in their publications. Considering the high number of articles, we divided this survey on seven categories:

  1. underground mapping,
  2. measure with airship,
  3. pose estimation and trajectory find with lidar,
  4. measure some features of the environment,
  5. measure of objects,
  6. lidar tests on UAV, and finally
  7. algorithms for odometry and comparison between lidar on UAV and UGV.

This is an ongoing survey. It will be regularly completed. We hope that this information will be useful to the robotics community. If we miss your paper or you find an error, don’t hesitate to contact us.

Legend: “O” means present and “-“ means absent of the paper.

Underground mapping

Article Subject UAV lidar Camera GPS IMU GNSS INS Test Note
(Michael et al., 2012) 3D mapping of earthquake-damaged building with collaborative ground and aerial robots Pelican quadrotor Hokuyo TM-30LX Microsoft Kinect - O - - Indoor UAV can travel on the UGV, ICP for mapping, SLAM for navigation
(Liu et al., 2015) Multiple UAV for 3D mapping Pelican quadrotor Hokuyo UTM-30LX - - O - - Simulation and Indoor test SLAM for navigation and path planning
(Ajay Kumar et al., 2017) Reconstruction of indoor building with UAV and lidar to classify pipes Phantom 3 advanced quadcopter Hokuyo UTM-30LX, Hokuyo URG-04LX-UG01 - - O - - Indoor ICP to merge data, use of Kalman filter to have position, classification of pipes by calculating the curvature of them
(Edwards, 2017) Scan of mine tunnel Asctec Hummingbird Z+F Imager S010 3D (terrestrial) Xtion Pro live - - - - Indoor Comparison between Microsoft Kinect Fusion Algorithm to terrestrial lidar
(Vempati et al., 2017) Indoor 3D mapping in real-time with UAV DJI M100 Leica Multistation MS50 (groundtruth for comparison) Intel RealSense R200 - DUO-MLX visual-inertial sensor - - simulation and Indoor test GPU parallelized SLAM to manage GPU memory ressources, 5mm resolution at 60Hz, extend Kalman filter, ICP for mapping
(Ozaslan et al., 2017) Inspection of tunnel and penstock with UAV DJI F550 Velodyne Puck Lite MChameleon 3 camera - PickHawk - - Indoor Local mapping for state estimation
(Mascarich et al., 2018) Inspection of nuclear sites and tunnel with UAV O Time of flight depth sensor Chameleon 3 camera - O - - Indoor Detection of radiation and it localization
(Papachristos et al., 2019) Autonomous Exploration and Mapping in Underground Mines Quadrotor O O - O - - Indoor -

Measure with airship

Article Subject UAV lidar Camera GPS IMU GNSS INS Test Note
(Pan et al., 2015) System to follow power lines in order to detect some issues Small helium airship O - O - - - Outdoor -
(Koska et al., 2017) Comparison between lidar on airship and camera on UAV Airship ACC15X, 12m of length, 2.8m of diameter (57m^3), 15kg of payload Sick LD-LRS1000 (2D rotating) Canon EOS 100D, FLIR SC645 IMAR iTracer-F200 - - O Outdoor Airship created by AirshipClub.com, issue of accuracy, issues with the structure but more accurate with airship on large area than UAV

Pose estimation and Trajectory

Article Subject UAV lidar Camera GPS IMU GNSS INS Test Note
(Downs et al., 2004) Better estimation of UAV pose with Laser than GPS Flying Eye LADAR system (UAV) and LMS-Z420i ground - - - - - Outdoor ICP to process 2 series of data, accuracy comparison between data obtained by UAV and terrestrial robot
(Hwang & Tahk, 2012) Measurement of UAV pose with lidar Simulation of airplane O - - O - - Simulation in order to compare 2 algorithms for localization lidar study the landscape, comparison between Kalman filter and Cross Correlation (which is better)
(Han et al., 2012) Measurement of the pose of a target relative to a UAV Small airplane O - O O - - Outdoor Pictures extracted of a database to test an algorithm for detection of outline, GPS and IMU to estimate position of UAV, ICP to process data, discussion about the number of ICP iteration and his effect on results
(Gee et al., 2017) Merge of some point clouds taken with lidar in order to use SLAM algorithm DJI Phantom Quadcopter Velodyne puck VLP-16 GoPro Hero 3+ - - - - Outdoor Reconstruction of data, static lidar on the ground (camera on UAV), curve fitting on points then ICP to process them and SLAM
(Shetty & Gao, 2017) GPS and lidar merge of data to correct error of GPS in urban environment Quadrotor Velodyne VLP-16 puck Lite - LEA-6T GPS Xsens-Mti-30 IMU - - Outdoor Modelisation of point cloud covariance error, detection of UAV pose by Unscented Kalman filter, matching between lidar data and 3D city model, ICP to match the point clouds
(Zhen & Scherer, n.d.) Test a method with lidar and SLAM algorithm cross-platform for UAV (2D-3D) DJI M100 DJI M600 Rotating Hokuyo Rotating Velodyne VLP-16 - - - - - Simulation and outdoor Use of Kalman filter (ESKF) and Gaussian Particle Filter (GPF), slow speed to avoid error on position
(Youn et al., 2018) Detection of environment in 3D to plan and watch over traffic in the sky and fly Cessma 208B ALS50-III - O - - O Outdoor Cut the world in cubic shape with fixed size (different level of cutting following 20 degrees of latitude), 20 level in all
(Gomes et al., 2018) Measure of the trajectory derivation with data of lidar on UAV Mikrokopter Quadro XL Hokuyo UTM30LX - - Microstrain IMU - - Indoor Methodology to detect some outline in point cloud, pose estimations

Measure some features of the environment

Article Subject UAV lidar Camera GPS IMU GNSS INS Test Note
(Liu et al., 2011) Analyze of the ground with UAV and lidar to detect and anticipate some landslide Helicopter lidar on helicopter O DGPS O - - Outdoor Process some point clouds and pictures
(Durst et al., 2011) Use of drone and lidar to study some proprieties of roads (type of vehicule, curvature, height...) Buckeye drone O O - - - - Outdoor The lidar measure the height of landscape, the roads are detected by processing on pictures
(Wallace et al., 2012) Search to detect some trees in forest (e.g., height, position, diameter) Oktokopter Droidworsc/Micropter AD-8 (terraLuma) Ibeo LUX laser scanner O O O - - Outdoor Kalman filter to have position
(Wallace et al., 2012) Measurement the effect of ground environment and parameters to take some data about trees Drone TerraLuma Ibeo LUX laser scanner O O O - - Outdoor -
(Candigliota et al., 2012) Analyze the structure of city after an earthquarter to avoid some other damages Quadrotor Leica Geosystems HDS 3000 - DGPS O - - Outdoor ICP to process the data
(Zhou et al., 2013) Use different altitudes and speed to take some data about landscape O O - O - - O Outdoor -
(Wallace, 2013) Test some new features to analyze the structure of forest in probabilistic way Drone TerraLuma Ibeo LUX laser scanner O O O - - Outdoor -
(Roca et al., 2014) Test of UAV with lidar to measure a ground Hisystems GmbH Hokuyo UTM-30LX - O O - - Outdoor Kalman filter to estimate the pose, tests , test carry out on a house
(Esposito et al., 2014) Measurement the features of trees (e.g., height, form) to validate the working of a lidar on a UAV Ultralight helicopter YellowScan lidar - O - - O Outdoor -
(Wallace et al., 2014) Measurement the influence of algorithms to detection and density of points on the accuracy to detect trees and their features Drone TerraLuma Ibeo LUX laser scanner O O O - - Outdoor -
(Ni et al., 2015) Do the inventory of forest with UAV and lidar Yun-5 aircraft Leica ALS 60 - - - - - Outdoor -
(Mandlburger et al., 2015) Analyze the features of waterway with UAV and lidar (dynamic, delimitation...) Ricopter UAV Riegl VUX-Sys, VUX-1, VQ-880-G Sony Alpha 6000 RGB - O O - Outdoor -
(Reiss et al., 2016) Scan and record some ruins with lidar on UAV Asctec Falcon 8, Sensefly e-Bee Faro Focus 3D S-120 (UAV), Optec 3D-HD ILRIS (terrestrial) Sony Alpha 6000 RGB - - - - Outdoor Merge of terrestrial data and flying photogrametry
(Guo et al., 2017) Scan forest with UAV and lidar 8 rotor UAV Velodyne puck VLP-16 - O Novatel IMU - - Outdoor Calibration of equipments, obtaining different features on forest
(Wei et al., 2017) Filter the effect of vegetation to measure the landscape 8 rotor UAV Riegl VUX-1 - O Trimble Applanix AP20-IMU - - Outdoor -
(Chiang et al., 2017) Reconstruction of building with UAV and lidar Small helicopter Velodyne lidar VLP-16 - C-Migits III O - - Outdoor New strategy of ICP in order to process the deformation of point cloud, use of Kalman filter for data, comparison with terrestrial lidar
(Chen et al., 2017) Measurement of the northern vegetation with UAV and lidar Quadcopter O - - - O - Outdoor Measurement of vegetation height, RTK GNSS use for localization, use of database lidar data, backcross of data to generate a map of measurments
(Sankey et al., 2017) Scan of forest with UAV and lidar Octocopter aircraft Velodyne HDL-32E (UAV), Riegl VZ-1000(terrestrial) - O - - O Outdoor Comparison with terrestrial lidar, determination of tree species founded according to the reflectance of laser, limited by the density of trees
(Christiansen et al., 2017) Scan and measure the height of croops with UAV and lidar DJI Matrice-100 quadcopter Velodyne VLP-16 - - VN-200 IMU Trimble BD920 GNSS - Outdoor Process the data to isolate croops and measure their height, high dependancy to GPS values
(Gawel et al., 2017) Do the scan of a disaster area with UAV and lidar, then send the data to some UGV for SLAM algorithm O O - - O - - Outdoor Use of extend Kalman filter for position, ICP to merge data from lidar on UGV and UAV
(Elaksher et al., 2017) Scan of croops or ground with UAV and lidar DJI S900 hexacopter Velodyne VLP-16 - O O - - Outdoor Classification of data (ground of vegetation)
(Putut Ash Shidiq et al., 2017) Topography of forest with UAV and lidar to differentiate ground and trees DJI Matrice-600 YellowScan lidar - O - - - Outdoor -
(Morsdorf et al., 2017) Topography of forest with UAV and lidar compare to terrrestrial lidar Scout B1-100 helicopter Riegl VUX-1UAV (drone), Riegl VZ1000 (terrestrial) - XTS xNAV550 GPS O - - Outdoor Speed of 4 m/s, error of 1 m on the height of trees
(Thiel & Schmullius, 2017) Comparison between point cloud obtained by camera on UAV and lidar on UGV, to find features of forest Geocopter X8000 Riegl VZ 1000 TLS (terrestrial) Sony NEX-7 RGB DGPS - - - Outdoor Frame of maxima, height of canopy and level of detection, speed of 8 m/s for UAV, comparison with terrestrial lidar, process of data to have features of forests
(Huang et al., 2018) Measure the propiety of see wave, change of coast and tide with UAV and lidar DJI S1000 Hokuyo UTM-30LX - - Xsens-Mti-30 IMU O - Outdoor RTK GNSS for localization, calibration and measurements of data, comparison of lidar data with pressure sensor Doppler Velocimetry (Son Tek ADV-Oceans)
(Cramer et al., 2018) Measure the ground and landscape with high accuracy by lidar on UAV (3-5mm) Ricopter multi-copter pateforme Riegl VUX-1LR Sony Alpha 6000 RGB - O O - Outdoor Speed of 8 m/s for UAV, altitude of 50m, comparison with terrestrial data by tacheometry
(Govedarica et al., 2018) Study of flood risk with UAV and lidar WingtraOno drone LMS-Q680i-Full (airplane) 42 MP Sony RX1RII O O - - Outdoor DEM technics (digital elevation models), lidar as reference for camera
(Hinterhofer et al., 2018) Use a UAV and lidar on nuclear disaster area Ricopter-M Riegl VUX-1UAV - - O O - Outdoor Mapping the field and measure the gamma rate of radiation
(Yan et al., 2018) Measure of forest production by UAV and lidar GV 1300 multirotor Velodyne VLP-16E - O IMU-IGM-S1 - - Outdoor Segmentation of trees by algorithms
(Liu et al., 2018) Measure the stucture of forest with UAV and lidar GV 1900 multi-rotor UAV Velodyne puck VLP-16 - O IMU-IGM-S1 - - Outdoor -
(Polewski et al., 2019) Methodology to have position of tree by using point cloud obtained by UAV and UGV Quadrotor Velodyne puck VLP-16 (UAV), velodyne puck VLP-16 (UGV) - GPS Novatel (UAV) IMU Novatel SPAN-MEMS (UAV) - POS (UGV) Outdoor Terrestrial lidar in bag carry by person during tests

Measure some objects

Article Subject UAV lidar Camera GPS IMU GNSS INS Test Note
(Lin & West, 2014) Measurement and scan of objects with oblique geomety Microdrone md4-200 Sensei MLS system (UAV), Ibeo Lux Laser (véhicule terrestre) O O O - - Outdoor Merge of data came from UAV and UGV
(Gu & Zhang, 2015) Locate some objects with a fast platform and lidar O O - O O - - Outdoor Increase the speed of algorithms with K-d tree and AK-d tree, altitude too high imply less performance
(Moore et al., 2017) Inspection of power lines with UAV Octorotor O - O - - - Outdoor Addition of lidar to avoid collision and make path planning, use of several drones to follow the lines, transformation of lidar data in polygominal shape, avoiding drone collisions in real time
(Teng et al., 2017) Inspection of power lines with UAV Hexarotor O - O - - - Outdoor Isolate of lidar the air vibrations, detection of sagging power lines
(Nikolov & Madsen, 2017) Inspection of wind turbine pale with UAV and lidar O RPlidar - - BNO055g-DOF IMU - - Outdoor Simplification in 2D of the pale shape
(Chen et al., 2018) Measure of distance anomaly between power lines and ground Drone mini helicopter Riegl VZ400 - O O - POS Outdoor Algorithm to differentiate ground and cable, measurement of cable curvature

lidar test on UAV

Article Subject UAV lidar Camera GPS IMU GNSS INS Test Note
(Li et al., 2015) Analyze vibrations of differents UAV in order to use a lidar on it Gasoline helicopter HDL-32 lidar - - O - - Outdoor Selection of an adapted rotor, measurement and attenuation of vibrations with IMU
(Tulldahl et al., 2015) Evaluate the accuracy of lidar on UAV Tarot-810 hexacopter Velodyne HDL-32E - - - - O Outdoor Process the data (dynamic calibration)
(Yang & Chen, 2015) Use of lidar on mini-UAV Rotor wing mini UAV Riegl LMS-Q160 - - - - - Outdoor Process of data to have the shape of objects founded by lidar(ICP), merge of shape, points and picture obtained by the pose of UAV and lidar
(Kasturi et al., 2016) Developpment of mini scanner for drone with MEMS (optical and mirror components) DJI Phantom II Playzer - - - - - Indoor Test of the prototype, different modes of scanning by laser, different modes to take measurements, objects tracking by laser, android bluetooth control of Playzer
(Mastrangelo et al., 2018) Obtain and access to 3D images in real time on UAV LASE drone ASC TigerCub camera, Arete AirTrac laser, Sentech color camera, Hood Tech gimbal - - - - - Outdoor Acquisition of data with pose correction
(Torresan et al., 2018) Integration of lidar on UAV then measure the performances Hexarotor LUX 4L - O - VN-300 GNSS O Outdoor Use of extend Kalman filter for position, calibration of measurements, study on accuracy and efficiency of GNSS
(Nasrollahi et al., 2018) Design lidar-equipped UAV for structural inspection DJI Matrice 100 Hokuyo UTM-30LX - - - - - Indoor Stationnary mode to test 3D mapping

Algorithms for odometry and comparison between lidar on UAV and UGV

Article Subject UAV lidar Camera GPS IMU GNSS INS Test Note
(Tulldahl & Larsson, 2014) Comparison of lidar data on UAV to terrestrial lidar Tarot-810 hexacopter Velodyne HDL-32E - O MEMS-IMU - - Outdoor Comparison with terrestrial lidar on vehicule and a static one
(Yousif et al., 2015) Description of algorithms and strategy use for odometry - - - - - - - - Survey
(Lawson, 2015) Merge of some point cloud obtained by multiple UAV Quadrocopter - Kinect RGB - - - - Indoor with 2 drones Generation of a point cloud given to a algorithm which merge data using ICP, need of several overlap to have better results
(Petras et al., 2016) Use of data obtained by UAV with lidar with a software call Grass GIS Airplane O Microsoft Kinect - - - - Outdoor Focus on process data, density of points test to decrease the number of redundant points
(Gašparović et al., 2017) Evaluation of data taken by UAV and lidar during non-optimal conditions (wind, cloud, density of points,...) DJI Phantom 4 Pro and airplane Pilatus P6 aircraft Optech ALTM Gemini 167 (airplane) FC6310 camera (drone) - - O - Outdoor Correlation between pictures and lidar data
(Persad & Armenakis, 2017) Comparison of approach to merge data collected by different systems (UAV lidar and terrestrial lidar) Geo-X8000 O - - - - - Outdoor Results depend a lot of point clouds configuration and parameters
(Kwon et al., 2017) Fast merge of atypical 3D data in different situations on construction site (lidar on UAV and terrestrial lidar) DJI Phantom 3 avanced O - - - - - Outdoor RTK for pose estimations
(Fuad et al., 2018) Influence of altitude on measure accuracy AL3 S1000 AL3-32 - O - - - Outdoor More the altitude increase (20m, 40m, 60m) more the precision decrease (RMS 0.323m, 0.450m, 0.616m)
(Park et al., 2019) Merge of data obtained by different platform and different sensors (on UAV and UGV) Quadrotor and UGV O (UGV) Camera (UAV) O O - - Outdoor ICP to process data, SLAM for UGV, elimination of redundant points then ICP, the drone should take picture between 30 and 90 degrees to optimize results

References

  1. Polewski, P., Yao, W., Cao, L., & Gao, S. (2019). Marker-free coregistration of UAV and backpack LiDAR point clouds in forested areas. ISPRS Journal of Photogrammetry and Remote Sensing, 147(July 2018), 307–318. https://doi.org/10.1016/j.isprsjprs.2018.11.020
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  3. Papachristos, C., Khattak, S., Mascarich, F., & Alexis, K. (2019). Autonomous Navigation and Mapping in Underground Mines Using Aerial Robots.
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  4. Govedarica, M., Jakovljevic, G., & Álvarez Taboada, F. (2018). Flood risk assessment based on LiDAR and UAV points clouds and DEM. Remote Sensing for Agriculture, Ecosystems, and Hydrology XX, October 2018, 102. https://doi.org/10.1117/12.2513278
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  25. Nikolov, I., & Madsen, C. (2017). LiDAR-based 2D Localization and Mapping System using Elliptical Distance Correction Models for UAV Wind Turbine Blade Inspection. Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Visigrapp, 418–425. https://doi.org/10.5220/0006124304180425
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  26. Kwon, S., Park, J. W., Moon, D., Jung, S., & Park, H. (2017). Smart Merging Method for Hybrid Point Cloud Data using UAV and LIDAR in Earthwork Construction. Procedia Engineering, 196(June), 21–28. https://doi.org/10.1016/j.proeng.2017.07.168
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  27. Elaksher, A., Bhandari, S., Carreon Limones, C. A., & Lauf, R. (2017). Potential of UAV lidar systems for geospatial mapping. Lidar Remote Sensing for Environmental Monitoring 2017, August 2017, 20. https://doi.org/10.1117/12.2275482
     Publisher    Bibtex source
  28. Putut Ash Shidiq, I., Wibowo, A., Kusratmoko, E., Indratmoko, S., Ardhianto, R., & Prasetyo Nugroho, B. (2017). Urban forest topographical mapping using UAV LIDAR. IOP Conference Series: Earth and Environmental Science, 98(1). https://doi.org/10.1088/1755-1315/98/1/012034
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  29. Guo, Q., Su, Y., Hu, T., Zhao, X., Wu, F., Li, Y., Liu, J., Chen, L., Xu, G., Lin, G., Zheng, Y., Lin, Y., Mi, X., Fei, L., & Wang, X. (2017). An integrated UAV-borne lidar system for 3D habitat mapping in three forest ecosystems across China. International Journal of Remote Sensing, 38(8-10), 2954–2972. https://doi.org/10.1080/01431161.2017.1285083
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