Calibration of kinematic LiDAR for Marine Infrastructure Inspection

Transcription

1 1/22 Calibration of kinematic LiDAR for Marine Infrastructure Inspection A. Picard, N. Seube, T. Touze Ocean Sensing and Mapping Lab. ENSTA Bretagne Brest FRANCE and CIDCO Rimouski CANADA, Qc Forum PPMD, March 20th 2012

2 2/22 Outline Motivation 1 Motivation 2 3

3 Some problems Two types of problems (encountered with all ranging devices (LiDAR, MBES) Synchronization (Positioning-LiDAR, IMU-LiDAR) : timing problems Boresight angles (IMU-LiDAR) : IMU and LiDAR misalignment Environment dependent calibration (acoustic, radiometric): not adressed in this talk Calibration data are geo-referenced, thus affected by position uncertainty LiDAR: Light Detection and Ranging MBES: Multibeam EchoSounder IMU: Inertial Motion Unit 3/22

4 Some problems Two types of problems (encountered with all ranging devices (LiDAR, MBES) Synchronization (Positioning-LiDAR, IMU-LiDAR) : timing problems Boresight angles (IMU-LiDAR) : IMU and LiDAR misalignment Environment dependent calibration (acoustic, radiometric): not adressed in this talk Calibration data are geo-referenced, thus affected by position uncertainty LiDAR: Light Detection and Ranging MBES: Multibeam EchoSounder IMU: Inertial Motion Unit 3/22

5 Some problems Two types of problems (encountered with all ranging devices (LiDAR, MBES) Synchronization (Positioning-LiDAR, IMU-LiDAR) : timing problems Boresight angles (IMU-LiDAR) : IMU and LiDAR misalignment Environment dependent calibration (acoustic, radiometric): not adressed in this talk Calibration data are geo-referenced, thus affected by position uncertainty LiDAR: Light Detection and Ranging MBES: Multibeam EchoSounder IMU: Inertial Motion Unit 3/22

6 Some problems Two types of problems (encountered with all ranging devices (LiDAR, MBES) Synchronization (Positioning-LiDAR, IMU-LiDAR) : timing problems Boresight angles (IMU-LiDAR) : IMU and LiDAR misalignment Environment dependent calibration (acoustic, radiometric): not adressed in this talk Calibration data are geo-referenced, thus affected by position uncertainty LiDAR: Light Detection and Ranging MBES: Multibeam EchoSounder IMU: Inertial Motion Unit 3/22

7 Some approaches in calibration 4/22 Methods based on target matching Known points, mapped from several points of view Adjustment of boresight angles in order to achieve target matching Used for LiDAR applications Surface matching : Digital Elevation Models (DEM) matching Used in both LiDAR and MBES applications (Vessel mounted LiDAR)

8 Some approaches in calibration 4/22 Methods based on target matching Known points, mapped from several points of view Adjustment of boresight angles in order to achieve target matching Used for LiDAR applications Surface matching : Digital Elevation Models (DEM) matching Used in both LiDAR and MBES applications (Vessel mounted LiDAR)

9 Autonomous Calibration 5/22 Motivated by land survey in hazardous environments Fusion of GPS, IMU, TLS data Accurate geolocalisation by GPS-IMU tight integration Target calibration

10 Time synchronization 6/22 Positioning - Ranging device GPS Pulse Per Second Most equipments take a PPS as input : Reduced latency IMU - Ranging device IMUs internal computation integrates navigation dynamics All IMUs have a specific latency, not always documented

11 Objective of Latency calibration 7/22 Airborne LiDAR: 0,1ms accuracy (source: Skaloud, Litchi, 2006, ISPRS) Vessel Mounted LiDAR: 0,5 ms (for centimeter accuracy for marine infrastructure surveys) IMU-Ranging device TOTAL latency definition: IMU time delay between physical measurement and data output Transmission delay to the acquisition device Stacking in acquisition buffers Acquisition software data assembling (orientation, ranging, position) for geo-referencing

12 Objective of Latency calibration 7/22 Airborne LiDAR: 0,1ms accuracy (source: Skaloud, Litchi, 2006, ISPRS) Vessel Mounted LiDAR: 0,5 ms (for centimeter accuracy for marine infrastructure surveys) IMU-Ranging device TOTAL latency definition: IMU time delay between physical measurement and data output Transmission delay to the acquisition device Stacking in acquisition buffers Acquisition software data assembling (orientation, ranging, position) for geo-referencing

13 Objective of Latency calibration 7/22 Airborne LiDAR: 0,1ms accuracy (source: Skaloud, Litchi, 2006, ISPRS) Vessel Mounted LiDAR: 0,5 ms (for centimeter accuracy for marine infrastructure surveys) IMU-Ranging device TOTAL latency definition: IMU time delay between physical measurement and data output Transmission delay to the acquisition device Stacking in acquisition buffers Acquisition software data assembling (orientation, ranging, position) for geo-referencing

14 Objective of Latency calibration 7/22 Airborne LiDAR: 0,1ms accuracy (source: Skaloud, Litchi, 2006, ISPRS) Vessel Mounted LiDAR: 0,5 ms (for centimeter accuracy for marine infrastructure surveys) IMU-Ranging device TOTAL latency definition: IMU time delay between physical measurement and data output Transmission delay to the acquisition device Stacking in acquisition buffers Acquisition software data assembling (orientation, ranging, position) for geo-referencing

15 Positioning free latency calibration n = (N, E, D): navigation frame, bs: kinematic LIDAR body frame, bi: IMU frame. M :target point, M same point viewed with rotational motion x n = RbI n RbI bs x S x n = RbI n (t dt)rbi bs x S where R n bi and RbI bs : DCM from bi to n and from bs to bi. 8/22

16 Latency calibration (cont d) 9/22 x n = R n bi RbI n (t dt)x n Assuming a constant angular velocity: Rn bi (t dt) = Rn bi d [ dt R bi n ].dt = (Id + dtω bi n/bi )RbI n thus x n = RbI n (Id + dtωbi n/bi )RbI n x n = x n + dtrbi n ΩbI n/bi RbI n x n = x n + dtω n n/bi x n

17 Latency calibration (cont d) 10/22 Target point shift due to the latency dt: = x x. n = dt Ω n n/bi x n = dt ωbi/n n x n = dt RbI n ωbi bi/n x n Finally, the latency estimation equation is : dt = n ω bi bi/n x n (1)

18 Motivation Which target point M? Best choice: spherical target (No Edge, Center can be determined from detection points with higher accuracy than LiDAR accuracy, Center estimation by variation of coordinate method (iterative least square, or non linear optimization) 11/22

19 Experimental set-up 12/22 Use different equipments : Leica HDS6200, IxSea Octans4 Use a classical hydrographic surveying acquisition software: Qinsy HDS6200-PC : Ethernet, PPS Octans4-PC: serial link, no PPS Alternate scans of the sphere: (-7, + 7) deg/sec IMU+LiDAR motion controlled by a Motion simulator (IXMotion TRI30)

20 Experimental set-up 12/22 Use different equipments : Leica HDS6200, IxSea Octans4 Use a classical hydrographic surveying acquisition software: Qinsy HDS6200-PC : Ethernet, PPS Octans4-PC: serial link, no PPS Alternate scans of the sphere: (-7, + 7) deg/sec IMU+LiDAR motion controlled by a Motion simulator (IXMotion TRI30)

21 Experimental set-up 12/22 Use different equipments : Leica HDS6200, IxSea Octans4 Use a classical hydrographic surveying acquisition software: Qinsy HDS6200-PC : Ethernet, PPS Octans4-PC: serial link, no PPS Alternate scans of the sphere: (-7, + 7) deg/sec IMU+LiDAR motion controlled by a Motion simulator (IXMotion TRI30)

22 Experimental set-up 12/22 Use different equipments : Leica HDS6200, IxSea Octans4 Use a classical hydrographic surveying acquisition software: Qinsy HDS6200-PC : Ethernet, PPS Octans4-PC: serial link, no PPS Alternate scans of the sphere: (-7, + 7) deg/sec IMU+LiDAR motion controlled by a Motion simulator (IXMotion TRI30)

23 Experimental set-up 12/22 Use different equipments : Leica HDS6200, IxSea Octans4 Use a classical hydrographic surveying acquisition software: Qinsy HDS6200-PC : Ethernet, PPS Octans4-PC: serial link, no PPS Alternate scans of the sphere: (-7, + 7) deg/sec IMU+LiDAR motion controlled by a Motion simulator (IXMotion TRI30)

24 Experimental set-up 12/22 Use different equipments : Leica HDS6200, IxSea Octans4 Use a classical hydrographic surveying acquisition software: Qinsy HDS6200-PC : Ethernet, PPS Octans4-PC: serial link, no PPS Alternate scans of the sphere: (-7, + 7) deg/sec IMU+LiDAR motion controlled by a Motion simulator (IXMotion TRI30)

25 Experimental results 13/22 Two sphere scans : Precision of center determination 0, 04mm give a latency precision estimate of 0, 06ms

26 Numerical results 14/22 FIFO buffer Sphere center Total IMU Latency due Residual size center SD latency latency to buffer latency mm 2.82 ms 2.35 ms ms 8 bytes mm 3.31 ms 2.35 ms 0.69 ms 0.27 ms 14 bytes mm 3.97 ms 2.35 ms 1.22 ms 0.40 ms Conclusion : Accuracy = 0,2ms, Precision = 0,06ms Applicable to any ranging device. Depend only on acquisition system and IMU configuration Configuration set-up of acquisition buffer size impact latency

27 Positioning free boresight calibration 15/22 Objective : set-up a IMU-LiDAR laboratory calibration procedure

28 Tripod boresight calibration 16/22 Sequence of static scans of a fixed tripod From cylinder cuts, we estimate C 1, C 2, C 3 as ellipse centers We reconstruct the tripod origin and get vectors OC i Then, we determine the boresight DCM

29 Ellipse center fitting 17/22 Most stable method : direct fit of a 5 parameter quadratic function.

30 Tripod orientation determination 18/22 d ij = C i C j, d 0j = OC j Distances are solutions to the non linear system d 2 Oi + d 2 Oi 2d Oi d Oj = d 2 ij, i = that can be solved by the Newton s method. Point O can be determined by the following system: 2x O (x 2 x 1 ) + 2z O (z 2 z 1 ) = d 2 O1 d 2 O2 + x 2 2 x z2 2 z2 1 2x O (x 3 x 1 ) + 2z O (z 3 z 1 ) = d 2 O1 d 2 O3 + x 2 3 x z2 3 z2 1 y 2 0 = d 2 O1 (x O x 1 ) 2 (z O z 1 ) 2

31 Boresight equation DCM matrix from (bs) to (bi), and RbI0 bs its a priori estimate E a micro-rotator operator : RbS bi = (I E)RbI0 bs DCM from IMU to (n), the navigation frame RbS bi R n bi Basic principle : Orientation of the tripod, written in the navigation frame is invariant, whatever the LiDAR-IMU orientation (e.g; lidar scnanning plan orientation) (RbI n ) 1RbS bi (X 1 S ) 1 = (RbI n ) 2RbS bi (X 1 S ) 2 Denoting by ε the boresight angle vector, the calibration adjustement equation becomes: (R n bi ) 1R bi0 bs (X S i ) 1 (R n bi ) 2R bi0 bs (X S i ) 2 = ( (R n bi ) 2( ) bi X i ) 2 (RbI n ) bi 1( X i ) 1 ε the which can be numericaly solved by iterative least square. 19/22

32 Numerical simulation results 20/22 Robustness analysis with ellipse center SD=1cm (over estimated)

33 Numerical simulation results (cont d) 21/22 Robustness analysis with ellipse center SD=1mm

34 22/22 Positionning errors propagates through calibration methods and alterate its accuracy Latency calibration method compatible with ALS requirements Positioning free IMU-LiDAR boresight calibration procedure Future work Validate the method through comparison between TLS and Vessel Mounted Lidar data in the framework of port infrastructure surveys Adapt the tripod method to multibeam echosounder calibration in the hydrographic survey framework