1. 1 Challenge the future Point Clouds from Lidar and Imagery – Status and Trends Mathias J.P.M. Lemmens Delft University of Technology, The Netherlands (MSc Geomatics for the Built Environment: (1) Geodata Acquisition Technology & (2) Geodata Quality) Senior Editor GIM International International Consultant (2014 -2015: WB, Kenya) M.J.P.M.Lemmens@tudelft.nl

2. 2 Challenge the future Racurs 2014 Conference, Hainan Features of Point Clouds and Functionalities of Processing Software ISPRS 1988 Congress, Kyoto A SURVEY ON STEREO MATCHING TECHNIQUES IGARSS'97, Singapore Accurate height information from airborne laser-altimetry ISPRS 1997, Stuttgart, Building detection by fusing airborne laser-altimeter DEMS and 2D digital maps. 1997 onwards: Over 100 papers in GIM International on Photogrammetry and Lidar

3. 3 Challenge the future Agenda General Developments Dense Image Matching on Oblique Images Multispectral Airborne Lidar Airborne Radar SLAM: Indoor 3D Modelling of Indoor Scenes

4. 4 Challenge the future Nucleus of Point Clouds

5. 5 Challenge the future Developments Applications are steadily growing Variety of Sensors create increasingly dense point clouds Variety of Processing Software How to tackle the storage and fast retrieval problem?

6. 6 Challenge the future DIM allows point densities similar to the ground sampling distance (GSD) of the imagery: GSD of 10cm  100 height points per square meter. Driving Forces - Programmable graphical processing units (GPUs) - New algorithms  Semi-global matching (SGM) algorithm introduced by Hirschmüller (2008) - Cheap computer power - Cheap digital cameras provide high-quality imagery, while large overlaps do not add to costs - Open source packages available from computer vision. Dense Image Matching (DIM)

7. 7 Challenge the future Aerial multi camera systems capture oblique and nadir imagery at the same time  full and intuitive view on both building footprints and facades beneficial for creating 3D city models. Maltese cross concept. Oblique Images

8. 8 Challenge the future Oblique images allow to extract denser point clouds with façades and building completed reconstructed Courtesy: Remondino et al., 2014

9. 9 Challenge the future

10. 10 Challenge the future Oblique Images Challenging for oblique imagery: Large scale variations Illumination changes Many occlusions Many questions are still open (Remondino et al., 2014 ): - when to use oblique imagery; - what are its strengths and weaknesses; - what is the optimal acquisition patterns for metric mapping; - how to deal with illumination and scale changes - which processing software is reliable and efficient? Need for performance measures of DIM software for oblique imagery (See Deuber et al., 2014)

11. 11 Challenge the future Airborne Lidar Routinely used for: - 3D modelling of urban areas - capturing boreal forests - Mapping Power Lines, etc. Status: - Increasing laser pulses frequencies (up to one million per second) - Multiple pulses in air - (full) waveform digitization Trends: - Multispectral Lidar - Photon Lidar - Lidar on a UAS Riegl-VQ-820 G

12. 12 Challenge the future The Titan, introduced December 2014, emits independent pulses in 3 narrow spectral bands. (Courtesy: Optech). Multispectral Lidar

13. 13 Challenge the future Multispectral Lidar The 3 beams do not pass the exact same path  the 3 multispectral points do not refer to the same terrain point. Envisioned applications - topographic surveying - shallow water bathymetry - environmental modelling - urban surface mapping - land cover classification. Combination through gridding  raster rather than a point cloud. False-colour raster image generated using Titan Lidar wavelength combinations (Courtesy of Laserdata GmbH and Optech).

14. 14 Challenge the future Further Improvements Each terrain point is recorded in each of the three wavelengths  Manufacturing a system where the beams overlap precisely and the returns are measured simultaneously. Multispectral Lidar

15. 15 Challenge the future BradarSAR Brazil Rockwell OrbiSAR P- band – wavelength 75cm – can penetrate the foliage and reach terrain underneath vegetation World´s largest aerial mapping project with X- and P-band. Products: DTMs, DSMs, X- and P-band orthoimages and 2880 maps at scale 1:50,000.

16. 16 Challenge the future P-band shows a road not visible in X-band Courtesy: Sambatti and Lübeck, 2015 P-band (75cm) X-band (3cm)

17. 17 Challenge the future Double bounce: P-band signals reflect on the terrain and only return as backscatter when reflecting again on tree trunks. Courtesy: Sambatti and Lübeck, 2014

18. 18 Challenge the future Radar DTMs and DSMs provide vegetation height maps and combined with biomass ground truth the biomass/carbon stocks can be estimated Courtesy: Sambatti and Lübeck, 2014

19. 19 Challenge the future SLAM GNSS signals are blocked indoors. Solution: ‘guessing’ the position and representing the space based on sensor data and prior knowledge. Guesses are iteratively refined using data collected while the robot is moving. Algorithms based on the iterative closest point (ICP) algorithm aimed at minimizing the difference between successive point clouds and the extended Kalman filter. Central is the use of landmarks; features distinct from the background. Positioning sensors: odometers, INS and lasers. Simultaneous Localisation And Mapping

20. 20 Challenge the future Founders of NavVis: Robert Huitl, Sebastian Hilsenbeck, Dr. Georg Schroth, Dr. Felix Reinshagen. 3D Mapping Trolley SLAM

21. 21 Challenge the future Point cloud overlaid with images of the shipping hall of the Deutsches Museum (Courtesy: Reinshagen et al., 2015)

22. 22 Challenge the future Moscow by Bicycle?

23. 23 Challenge the future Thank you so much for your attention.