linear lidar versus geiger-mode lidar: impact on data properties

Linear LIDAR versus Geiger-mode LIDAR: Impact on data properties and data quality

A. Ullrich*, M. Pfennigbauer*

*RIEGL Laser Measurement Sytems GmbH, Riedenburgstrasse 38, 3580 Horn, Austria

ABSTRACT

LIDAR has become the inevitable technology to provide accurate 3D data fast and reliably even in adverse measurement situations and harsh environments. It provides highly accurate point clouds with a significant number of additional valuable attributes per point. LIDAR systems based on Geiger-mode avalanche photo diode arrays, also called single photon avalanche photo diode arrays, earlier employed for military applications, now seek to enter the commercial market of 3D data acquisition, advertising higher point acquisition speeds from longer ranges compared to conventional techniques. Publications pointing out the advantages of these new systems refer to the other category of LIDAR as „linear LIDAR“, as the prime receiver element for detecting the laser echo pulses - avalanche photo diodes - are used in a linear mode of operation. We analyze the differences between the two LIDAR technologies and the fundamental differences in the data they provide. The limitations imposed by physics on both approaches to LIDAR are also addressed and advantages of linear LIDAR over the photon counting approach are discussed.

Keywords: LIDAR, Geiger mode, laser scanning, airborne sensing, point clouds

1. INTRODUCTION LIDAR in its traditional form as time-of-flight measurement with short laser pulses and a photodetector operated in the linear regime has become the inevitable technology to provide survey-grade 3D in a vast variety of applications. Applications include in the field of static laser scanning, e.g., acquiring data indoors, and providing as-built surveying of industrial sites or long-range laser scanning in open pit mines. In the field of so-called kinematic laser scanning from a broad range of platforms (land based vehicles, ships and all kind of aircrafts, all of these manned and unmanned) application include acquiring 3D data in corridors or over large extended areas of thousands of square kilometers. One of the specific strengths of LIDAR technology, in contrast to e.g. photogrammetry, is it multi-target capability enabling the penetration of vegetation to reveal objects below the canopy or to provide data from the ground for deriving a high resolution digital terrain model.

These traditional LIDARs come in two flavors, with so-called discrete returns based on analog signal detection and with so-called echo digitization with subsequent offline full waveform analysis or online waveform processing. The echo-digitizing LIDAR systems do not only provide highly accurate point clouds, but also a significant number of additional valuable attributes per point. These attributes include calibrated amplitudes and calibrated reflectance readings for every echo, but also attributes derived from the shape of the echo waveforms itself.

LIDAR systems based on Geiger-mode avalanche photo diode arrays earlier employed for military applications, now seek to enter the commercial market of 3D data acquisition in airborne applications from high altitudes, advertising tremendously higher acquisition speeds from longer ranges compared to conventional techniques [1]. Publications pointing out the advantages of these new systems refer to the other category of LIDAR as „linear LIDAR“, as the prime receiver element for detecting the laser echo pulses - avalanche photo diodes - are used in a linear mode of operation.

Subsequently we analyze the differences between the two LIDAR technologies and the fundamental differences in the data they provide, especially with respect to the capability of penetrating the canopy of dense vegetation and to the achievable accuracy level.

The information on Geiger Mode LIDAR presented in this paper is based on calculations and simulations carried out with the authors’ best efforts. The parameters used for the calculations and simulations were obtained from publicly available sources. The authors have executed their best efforts to respect any and all possible copyrights.

Laser Radar Technology and Applications XXI, edited by Monte D. Turner, Gary W. Kamerman, Proc. of SPIE Vol. 9832, 983204 · © 2016 SPIE

CCC code: 0277-786X/16/$18 · doi: 10.1117/12.2223586

Proc. of SPIE Vol. 9832 983204-1

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3. BASIC GEIGER-MODE LIDAR PROPERTIES Geiger-Mode LIDAR (GmLIDAR) advertised for commercial surveying applications utilize not a single photodetector as the current linear-mode LIDAR systems for airborne surveying but an array of avalanche photo diodes, typically with 32 x 128 pixels [8]. Each of the APDs is biased above the breakdown voltage so that a single photon may trigger the APD with a certain detection probability.

In the subsequent discussion of GmLIDAR we neglect the dark count rate of any actual APD array and also photons due to solar background radiation, i.e., we assume to have an ideal APD array operated at nighttime.

Figure 4 depicts a very simple block diagram of a GmLIDAR indicating the signals and data rates to be expected in the various regimes ranging from optical (left in red), analog (in the middle in blue) and the digital regime (right in black).

A Geiger-mode array is usually activated sometime after the laser pulse has been emitted. The time period for which the array is active, i.e., when the APDs are biased above breakdown voltage, is denoted as a range gate. The delay of the start of the range gate with respect to the emitted laser pulse is set according to an a priori knowledge based on the flight path, the scan pattern of the LIDAR system, and the terrain data has to be acquired on.

Figure 4. Simplified block diagram of a Geiger-Mode LIDAR, again depicting the optical regime (red), an analog electrical regime (blue), and a digital regime (black). Signals are examples for a single pixel of the APD array.

In contrast to linear LIDAR, GmLIDAR in its current state of development triggers each pixel of the receiver array maximally once per laser pulse. In case the return of the first target contains some photons, the first target will most probably trigger the pixel, and the subsequent returns from the second and third target will be lost. Instead of an ADC, the GmLIDAR utilized an array of TDCs (time-to-digital converters) providing the time delay between the start of the range gate and receiving the first photons. For typical values of timing resolutions (0.5 ns), range gate lengths (4 µs), and pulse repetition rates (PRR) (50 kHz) the amount of data is about 400 MByte/sec. In the online part of the digital regime there is, in contrast to linear LIDAR, no signal detection as this already happens already in the APD array. Signal estimation in GmLIDAR is rudimentary as it just provides the temporal position of the trigger event and thus range, but no information of the signal strength, i.e., the number of photons that have actually triggered the pixel, or on the pulse width of the received echo pulse.

4. SPATIAL SAMPLING OF TARGET OBJECTS Current state-of-the-art linear LIDAR systems used in airborne 3D surveying acquire data sequentially at high laser PRRs of several hundred kilohertz. Depending on height above ground (above ground level, AGL) and platform speed the measurement density typically varies between a few 100 measurements per square meter – usually addressed as


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5. WAVEFORM INFORMATION AND MULTI-LOOK PROCESSING The key to the best possible multi-target resolution and thus penetration of vegetation in airborne laser scanning with linear LIDAR is echo digitization and subsequent full waveform analysis. Figure 6 shows an example waveform when measuring into dense vegetation. The first pulse/peak may represent the top of the canopy, the last peak bare earth and the intermediate peaks the different layers in the vegetation.

Figure 6. Example waveform from a RIEGL LMS-Q680i when measuring into dens vegetation. Dots represent the samples from echo digitization, the solid curve the reconstruction by Gaussian decomposition.

It seems difficult to penetrate vegetation with a detector which triggers only once per laser pulse at the first few photons arriving at the detector. An approach to gain penetration capability is to adjust the detection in the APD array in a way that for every single illumination the detection probability for a single target return is so low, that there remains a non-zero detection probability for all subsequent targets [12]. In order to achieve a high detection probability for every target the scene has to be illuminated numerous times, i.e., to have numerous looks (multi-look) onto the same spot on the ground. This can be easily achieved for a stationary GmLIDAR system looking in the same direction in space all the time. In doing so, the temporal integration over all events gives a histogram of detections over range, which resembles a waveform from a linear LIDAR system from a single acquisition. However, for a kinematic acquisition from a fast moving airborne platform having numerous illuminations of the same spot imposes a severe challenge. For the commercial GmLIDAR system it is claimed that every spot on the ground is illuminated hundreds of times [1]. Subsequently, we derive the number of looks of a commercial LIDAR system for the advertised acquisition parameters as summarized in Table 1.

Table 1. Parameters published for a commercial GmLIDAR to acquire 8 pts/m² [1].

above ground level AGL 27,000 ft FOV of single pixel iFOV 35 µrad platform speed v 290 kts array dimensions - 32 x 128 FOV of scanner α 30 deg laser repetition rate PRR 50 kHz

(a) (b) Figure 7. Deriving the number of looks for an airborne GmLIDAR with a Palmer scan. For symbols refer to Table 1 and text.


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The size of the footprint (projection of the APD array) on the ground is also calculated easily as shown in Figure 7(b). D1 and D2 denote the size across the scan line and along the scan line in the center of the swath. The number of looks of a spot on the ground in the center of the swath within a single line is given by D2/d2, the number looks of consecutive lines is D1/d1. The formula derived for the number of looks, Nlooks, reveals that Nlooks is independent of the rotational speed of the scanner, rps, as long there is overlap of footprints along the scan line and from scan line to scan line. The number of looks can be changed by the operator only by changing the acquisition parameters AGL and v, as all the other parameters are fixed by system design. For the advertised acquisition parameters of Table 1 the number of looks is just about 10 but not hundreds of looks as claimed. The number of looks as a function of AGL is shown in Figure 8 on the left. On the right the number of looks is given as a function of the scan angle. In the center of the overlap of two neighboring scan swaths at 50% side overlap, the number of looks is 12 compared to 10 in the center of the swath.

Figure 8. Left: number of looks as a function of AGL and the system design parameter iFOV for a platform speed of 290 kts. Right: number of looks versus the scan angle (0 deg and 180 deg in the center of the swath, 90 deg and 270 deg the edges of the swath).

6. DETECTION PROBABILITY AND PENETRATION OF VEGETATION The detection probability for both systems, GmLIDAR and linear LIDAR strongly depends on the number of photons received within a single echo pulse. Subsequently, we estimate the number of photons received from a white diffusely reflecting target for the acquisition example advertised to achieve 8 pts/m2, i.e, AGL 27,000 ft and 290 kts platform speed for the GmLIDAR and AGL 3,280 ft and 117 kts for the linear LIDAR system [10] taking into account the system parameters like laser power, laser pulse repetition rate, receiver aperture and assuming a visibility of 23 km by making use of the LIDAR equation. For the estimation for the GmLIDAR published system parameters have been used [1, 13]. Table 2 summarizes the parameters used.

Table 2. System parameters used to estimate the number of received photons.

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Proc. of SPIE Vol. 9832 983204-10

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Proc. of SPIE Vol. 9832 983204-11

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GL 1,000 m onThe number oThe example isd with a highflectance for aon probability

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Proc. of SPIE Vol. 9832 983204-12

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em, as a closer

ure 12. The well below

s -d e h e a e r

Proc. of SPIE Vol. 9832 983204-13

For the linear LIDAR RIEGL LMS-Q1560 simultaneous acquisition of data on wire targets and on the ground beneath vegetation imposes no challenge, as shown by the example data in Figure 18, showing a perspective view of the point cloud with color-coding according to height on the left, and a cross section through the data on the right (wires of the power line show up as dots in the right hand side of the image).

Figure 18. Perspective view of data acquired with RIEGL LMS-Q1560 on two power lines and group of trees (left) and a cross-section through the same data set (right) demonstrating the capability to acquire data on low-cross-section targets like wires and ground beneath vegetation simultaneously.

8. MEASUREMENT ACCURACY AND MEASUREMENT NOISE Measurement accuracy and measurement noise of a LIDAR system are determined by a large number of phenomena, like noise within the receiver, background noise, shot noise of the optical signal itself, trigger walk due to the finite bandwidth of the laser pulse and the receiver, beam walk due to atmospheric turbulence and inhomogeneity to name just a few. Specific error sources in LIDAR are ranging noise and systematic ranging error. In echo-digitizing linear LIDAR systems, ranging is done by estimating the temporal position of a received echo signal, which in turn is based on digital signal processing schemes. This proves to give a very low change of the estimated range versus the signal strength of the echo signal over a very wide dynamic range and also a low ranging noise. Typically for these airborne linear LIDAR systems the trigger walk and the range measurement noise is about 20 mm. In non-echo-digitizing linear LIDAR systems, so-called discrete return systems, the inherent trigger walk imposed by the detection scheme is compensated for by estimating the amplitude of the echo signal and to correct for by adding a correction value from a look-up table.

In GmLIDAR systems an additional effect contributes to a significant ranging error on flat tilted surfaces: as the number of photons per return echo is quite low, the temporal position of the photon actually triggering the pixel of APD array is unknown. Thus a large range noise is to be observed. Furthermore, as the arrival times of the photons follow a Poisson process and the detector triggers just once per laser pulse, the early photons have higher detection probabilities than the ones arriving later, giving rise to a systematic range error. In contrast to discrete-return linear LIDARs, GmLIDAR does not provide any information on signal strength, thus systematic trigger walk cannot be compensated for at all.

Proc. of SPIE Vol. 9832 983204-14

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Proc. of SPIE Vol. 9832 983204-15

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[1] RhoReal

[2] RIEG[3] Ullri

full w[4] Ullri

Scan[5] Wag

Nov& R

[6] PfenRada

[7] RIEG2012

[8] CliftTech

. Range noise (AR (blue) on a vthe linear LIDA

nd performancpeed, oblique oise and systesystems, espef pick up echoe merits of GMn targets submcess the range

DAR.

ads, R., “Geility 2015, AusGL 2016a, wwich, A., Reichwaveform anaich A., Pfennnning” Proceegner, W., Ullrvel Small-FootRemote Sensinnnigbauer M.,ar TechnologyGL 2016a2.pdf , retrieveton, W.E, ethnology and A

(left) and systemvertical flat targAR is negligible

ce of linear Lviewing ang

ematic error. Vcially when f

oes from the gMLIDAR syst

merged by vege noise and sy

iger-mode Listria ww.riegl.com hert, R., Riegalysis”, Proceenigbauer, M.: edings of the 5rich, A., Ducitprint Full-Wag, 60 (2006), , Ullrich, A.: y and Applicaa, http://wed March 201t al.: “MediuApplications X

matic range erroet at an angle o, the specified a

9.LIDAR and G

les, vegetatioVegetation peflying low inground underntems, measuregetation will bystematic erro

R

DAR mappin

l, J.,: “High redings of SPIE“Echo Digiti

53rd Photogramc, V., Melzeraveform Digitpp. 100 – 112“Three-dime

ations XIII, 16www.riegl.com

6 um Altitude XX, Proc. of S

or (right) for theof incidence of 7accuracy of 2 cm

SUMMAReiger Mode Ln penetrationenetration for n order to incneath the canoements to powbe improved br on vertical f

REFERENCE

ng: High den

resolution laseE Vol. 5791, pization and Wmmetric Week, T., Studnicktising Airborn

2, 2006 ensional laser6 April 2008

m/uploads/tx_p

Airborne GSPIE Vol. 946

e RIEGL LMS-75 deg derived m is given for t

RY LIDAR have b, surveying ogeneration o

crease the chopy becomes ewer lines will sbut still compaflat targets are

ES

sity, high vo

er scanner wipp.82-88, Ma

Waveform Anak, September 2ka, N.: “Gaussne Laser Scan

r scanners wi

pxpriegldownl

eiger-mode M65, SPIE, 2015

-Q1560 linear Lby simulation. the RIEGL LM

been analyzedof man-made of digital elevhance to captuextremely lowshow a detectiaratively spare considerably

olume airborn

ith waveform rch 28th – Apralysis in Airb2011, Stuttgarsian Decompo

nner”, ISPRS J

th echo digit

loads/10_Data

Mapping Lid5

LIDAR (green) As the systema

MS-Q1560.

d and comparstructures likeation models ure wires. In

w. From high aion probabilitse. Due to the

y worse for Gm

ne 3D imagin

digitization fril 1st 2005, Oborne and Terrt osition and CaJournal of Ph

tization”, Vol

aSheet_LMS-

dar System”,

and a

atic range

red in view oe power linesis limited forthis case the

altitudes whenty of 3% whilee nature of themLIDAR than

ng”, Capturing

for subsequenOrlando

rrestrial Laser

alibration of ahotogrammetry

l. 6950: Laser

Q680i_28-09

Laser Radar

f s, r e n e e n

g

nt

r

a y

r

-

r

Proc. of SPIE Vol. 9832 983204-16

[9] Ullrich, A. Sampling the World in 3D by Airborne LIDAR – Assessing the Information Content of LIDAR Point Clouds, Photogrammetric Week, Stuttgart, 2013.

[10] [RIEGL 2016a, http://www.riegl.com/uploads/tx_pxpriegldownloads/DataSheet_LMS-Q1560_2015-03-19.pdf , retrieved March 2016]

[11] Romano, M.: “A New Industry Standard: Commercial Geiger-mode LIDAR”, LIDAR News webinar, March 24th, 2015

[12] Fried, D.G.: “Fast, Cost-Efficient Airborne 3D Imaging With Geiger-mode Detector Arrays”, MIT RLE & 3DEO, Inc. ILMF 2015, February 23, 2015, Denver, CO

[13] Clifton, W.E, et al.: “Medium Altitude Airborne Geiger-mode Mapping Lidar System”, Laser Radar Technology and Applications XX, Proc. of SPIE Vol. 9465, SPIE, 2015

Proc. of SPIE Vol. 9832 983204-17

linear lidar versus geiger-mode lidar: impact on data properties

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