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Licensed Unlicensed Requires Authentication Published by De Gruyter December 16, 2020

Temporal calibration and synchronization of robotic total stations for kinematic multi-sensor-systems

Tomas Thalmann and Hans Neuner


Despite the increasing interest in kinematic data acquisition, Robotic Total Stations (RTSs) are still relatively seldom used. No matter if Mobile Mapping Systems or Control & Guidance, GNSS is mostly used as position sensor, which limits the application to outdoor areas. For indoor applications, a combination of relative sensors is usually employed. One reason why RTSs are not used is the challenging time referencing and synchronization. In this work we analyze the challenges of a synchronized kinematic application of RTSs and present solutions.

Our approach is based on a wireless network synchronization to establish a precise temporal reference frame. The achievable synchronization quality is thoroughly examined. In addition we develop a kinematic model of spherical measurements, that incorporates timing related parameters. To estimate these parameters we propose a temporal calibration utilizing an industrial robot. Both parts of our approach are evaluated using a test setup of two total stations, proofing an overall synchronization accuracy of 0.2 ms. An overall horizontal kinematic point accuracy of 2.3 mm reveals the potential of sufficiently synchronized RTSs.

Appendix A Network Time Protocol

The Network Time Protocol (NTP) is used to distribute a reference time system in a computer network. The Protocol’s algorithms can be divided into three steps:

  1. Definition of the reference servers.

  2. Communication with the reference servers takes place on a regular basis (the polling interval) via UDP packets (User Datagram Protocol) on port 123. The timestamps in these packets are then used to estimate the difference (Offset and Drift, see (3)) to the reference time system.

  3. The local clock is corrected in real-time based on the result of the previous step.

The NTP communication is visualized in Figure 16. From the four timestamps (T1 to T4), computer A can calculate the offset θb to reference clock B according to (16). This process is performed twice at each observation time (timestamps T5 to T8).


This approach assumes that the transmission duration of the packet is direction-independent. In addition, the round trip delay δb is used to calculate a measure for the (doubled) transmission time. The greater δb, the higher the probability that the transmission time will differ on the outward and return paths. Based on the synchronization distance (=δb2) and the transmission jitter (variability of the synchronization distance) the state of the controller clock xtc (offset dtc and drift fc, see Section 2) is estimated from the offset observations θb to n reference servers using robust evaluation methods:


where θi and δi are the observations to the ith reference server, given by (16). A represents the core NTP algorithm and PNTP is a set of internal parameters.

Figure 16 NTP on-wire communication including timestamps (after [31]).

Figure 16

NTP on-wire communication including timestamps (after [31]).

To summarize, the achievable quality of synchronization using NTP depends on a variety of factors. 1) The reference servers and how precise they are in timekeeping. 2) Quality of the Ethernet transmission. This has mainly to do with the direction-independence assumption (symmetry) and is affected by Ethernet or Wifi, network topology and network traffic. 3) Local influences on the client. These result primarily from the Variable Frequency Oscillator (VFO) serving as the time-source in most computer-based systems. The frequency of the generated sinusoidal waveform depends on voltage, temperature and aging of the VFO. It follows, that CPU load also affects the synchronization result too.

Appendix B Taylor series expansion

The derivation of the first-order Taylor series is shown exemplarily for the x-coordinate (eq. (8), leaving out x0). Formulating x(tj) as a function of R and D using the following replacements




The first-order Taylor series expansion about D0, R0 (with RΔ in units of radian) is:


Computing the derivatives and rearranging gives:






Appendix C Model equality

Starting from (10) (again leaving out x0):


Using the cosine expansion and small angle approximation for ω(ta)δt for the cosine term in (10) gives:


Thus, (10) gives (neglecting second order terms):


which corresponds to (20).

Appendix D Common spatial reference frame

For the temporal calibration developed in Section 5 one necessary prerequisite is a common spatial reference frame shared between the reference IR and the RTS under test. This is established by estimating the transformation parameters from the IR coordinate frame u to the RTS coordinate frame r. In the same step the mounting vector tpe describing the prism center w. r. t. the robots end-effector frame e is determined. The transformation model is given in (24), adopted from [17]. The involved parameters x and observations l are described in Table 4.


Table 4

Parameters and Observations of the Transformation Model (24).

tprlPrism position measured by the RTS, tpr=epnphpT
teulPosition of the robot end-effector w.r.t to the robot base frame, teu=xeyezeT
ReulMeasured orientation of the robot end-effector frame w.r.t to the robot base frame. Reu=R(ϕ,θ,ψ)
RurxRotation angles α, β, γ forming the rotation matrix Rur from robot base frame to RTS-frame.
tpexPosition of the prism w. r. t. the robot end-effector frame, tpe=mxmymzT
turxOrigin of the robot base frame w. r. t. the RTS-frame, tur=eunuhuT

The corresponding points for a fully automated process must be chosen in a way such that the prism orientation remains constant. Because this leads to correlations between mounting vector tpe and translations tur a semi-automated process was designed, to reduce such correlations. It consists n=52 static poses and requires human interaction at two points in time.

Figure 17 Correlations of estimated transformation parameters.

Figure 17

Correlations of estimated transformation parameters.

Table 5

Results of parameter estimation for a real data set.

α0.4129 gon4.9 mgon
β0.1420 gon5.1 mgon
γ−1.5843 gon4.5 mgon
eu16.15227 m0.09 mm
nu−0.55588 m0.06 mm
hu−0.59648 m0.07 mm
mx0.43 mm0.06 mm
my−65.36 mm0.07 mm
mz137.40 mm0.06 mm

For each pose i=1n it gives 9 observations li=xeyezeϕθψepnphpT with the corresponding variance-covariance-matrix Σlli:


With σϕu=1mgon for the orientation accuracy of the robot end-effector, σru=0.18mm for the robot position accuracy, and Σir for the propagated point accuracy for the measured RTS positions this gives the model for a Least Squares Adjustment with condition equations (Gauss-Helmert-Model) to estimate 9 parameters.

The results are given in Table 5 and the corresponding correlation matrix is shown in Figure 17. With this results a sufficient transformation accuracy has been achieved for the subsequent calibration.


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Received: 2019-11-18
Accepted: 2020-11-18
Published Online: 2020-12-16
Published in Print: 2021-01-27

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