Patent Application
20230184551
The present disclosure pertains to a surveying system for measuring the position of a point using a survey pole and to a computer-implemented method for controlling such a surveying system. Particularly, the disclosure pertains to a surveying system comprising a pole equipped with an inertial measurement unit (IMU), wherein data from the IMU is used to derive motion patterns of the pole and wherein the surveying system automatically performs actions or workflows associated with the derived motion pattern.
A motion state can be a special case of a motion pattern. For instance, in a case in which the pole rests in a certain position over a certain period of time, so that the IMU detects no or basically no movements during this period of time, this lack of movements and the period of time without movements can be regarded as a motion state as a special case of a motion pattern.
In many geodetic applications, points are surveyed by positioning specially configured target objects at them. These target objects usually comprise a pole having a targetable marking, reflector or prism for defining the measurement distance or the measuring point. Using a geodetic surveying apparatus, such as a total station, a relatively large number of such target objects can be surveyed. In other geodetic applications, the pole comprises a GNSS antenna—i.e. either in addition to a reflective target or as an alternative.
Modern total stations have microprocessors for digital post-processing and storage of acquired measurement data. These devices are generally produced in a compact and integrated design, usually with coaxial distance and angle measurement elements as well as calculation, control and storage units integrated in one device. Often, means for motorizing the target optics, for reflectorless distance measurement, for automatic target search and tracking and for remote control of the entire apparatus are integrated. Total stations known from the prior art furthermore have a radio data interface for setting up a radio link to external peripheral components, for example to a data acquisition apparatus which, in particular, may be formed as a hand-held data logger, remote control unit, array processor, notebook, small computer or PDA. By means of the data interface, measurement data acquired and stored by the total station can be output to external post-processing, and externally acquired measurement data can be read into the total station for storage and/or post-processing. Also, remote control signals for remote control of the total station or of another external component, particularly in mobile field use, can be input or output, and control software can be transferred into the total station.
During regular operation of a survey pole—independent of whether the pole comprises a GNSS antenna or is operated together with a surveying device, the operator at some time needs to put the pole aside, e.g. have the pole rest against a wall or similar object in the surrounding or against his own shoulder, or drop the pole to the ground. This need arises whenever the operator needs to have both hands available, e.g. while using a handheld device or for marking a previously measured point on the ground.
In order to reduce energy consumption, it would be desirable if during this time, some or all components of the pole or the surveying system, e.g. a GNSS antenna or a total station, could be switched off or turned into a sleep mode automatically, and would be switched on again automatically if the operator resumes the surveying operation.
While the pole is put aside, a surveying instrument tracking the pole might lose its sight at the pole's reflective target. Fast and automatic re-locking of the pole's reflective target by a total station after line of sight has been lost is a known problem. Conventionally, total stations may simply extrapolate the last known movement if line of sight is lost for up to five seconds and then wait for one second at this position. It would be desirable, in order to reduce energy consumption of the surveying instrument, if a search for the reflective target would only be performed when the operator resumes the surveying operation. Also, it would be desirable to improve the speed of resuming the surveying operation, by quickly relocking to the reflective target automatically when the pole is moved back to its operating position.
Surveying systems, in which a survey pole comprises or is equipped with an IMU, are generally disclosed, for instance, in EP 2 909 579 B1 and U.S. Pat. No. 10,234,827 B2.
It would thus be desirable, if motions, motion patterns and/or motion states of the pole would be detectable in real time and could be used to automatically trigger certain actions or workflows of the surveying system.
It is therefore an object of the present disclosure to provide an improved surveying system comprising a survey pole.
It is another object of the present disclosure to provide such a surveying system that is easier to operate. It is a particular object to provide such a surveying system which can be operated by performing certain movements with the pole, particularly by repeating a pre-defined motion pattern.
It is another object of the present disclosure to provide such a surveying system that has a reduced energy consumption. It is a particular object to provide such a surveying system which can be automatically powered-down while the pole is not used.
It is another object of the present disclosure to provide such a surveying system which operates faster. It is a particular object to provide such a surveying system which automatically resumes measurements after a break.
At least one of these objects is achieved by the surveying system as described herein and/or the method as described.
A first aspect pertains to a surveying system for measuring the position of a measuring point. The surveying system comprises:
According to this aspect, the system comprises a motion tracker configured to receive the IMU data and to derive, based on the IMU data and in real time, motions and/or motion patterns—including motion states—of the survey pole, wherein, if a derived motion or motion pattern corresponds to a defined (e.g. pre- or user-defined) motion pattern (or motion state) of the survey pole, the surveying system is configured to automatically perform an action associated with the defined motion pattern.
The position giving means of the survey pole for instance may comprise a retroreflector, e.g. in the form of a prism, and/or a GNSS antenna.
According to one embodiment of the surveying system, the motion tracker is configured to generate motion data regarding derived motions or motion patterns and to provide the motion data to the control and evaluation unit, wherein the control and evaluation unit is configured to determine, based on the motion data, whether a derived motion or motion pattern corresponds to one of a plurality of defined motion patterns, and to issue a command to perform the action associated with the determined defined motion pattern to a unit or device of the surveying system, the action associated with the determined defined motion pattern relating to a function of said unit or device.
According to one embodiment, a database of the surveying system comprises a plurality of different defined motion patterns of the survey pole (e.g. either pre-defined or user-defined motion patterns), each defined motion pattern being associated with an action of the surveying system, particularly wherein the control and evaluation has access to the database.
In one embodiment, the control and evaluation unit comprises the database.
According to another embodiment of the surveying system, at least one defined motion pattern is a user-defined motion pattern, and the action associated with said user-defined motion pattern is a user-defined workflow of the surveying system.
In one embodiment, upon selection by a user, the system is configured to run, a definition process, and the control and evaluation unit is configured to determine, based on motion data received during the definition process, the user-defined motion pattern and to associate the determined user-defined motion pattern to the user-selected workflow.
According to another embodiment of the surveying system, the position giving means comprise a retroreflector, e.g. a prism, and the surveying system comprises a surveying device, e.g. a total station or a tachymeter, that is configured to measure positional parameters of the retroreflector comprising angles and a distance to the retroreflector and to derive a referenced position of the retroreflector.
In one embodiment, the survey pole and said surveying device are configured to establish a remote data connection with each other, and the action associated with the determined defined motion pattern relates to a function of the surveying device.
In one embodiment,
According to another embodiment of the surveying system, the position giving means comprise a GNSS antenna, and the surveying system comprises a GNSS processing unit configured to process output signals of the GNSS antenna and to derive a referenced position, orientation and/or velocity of the GNSS antenna based on the output signals.
In one embodiment, the action associated with the determined defined motion pattern relates to a function of the GNSS antenna and/or the GNSS-processing unit, wherein the motion tracker is configured to generate motion data regarding derived motions or motion patterns and to provide the motion data to the control and evaluation unit, and the control and evaluation unit is configured to determine, based on the motion data, whether a determined motion pattern corresponds to one of the defined motion patterns, and configured to issue a command to perform the action associated with the detected defined motion patterns to the GNSS antenna and/or the GNSS-processing unit, respectively.
According to another embodiment of the surveying system, the motion tracker is configured to determine at least inertial velocity data as part of the motion data. For instance, the motion tracker may be further configured to determine
According to another embodiment, the system is configured to establish a data connection with a remote server computer and to provide IMU data and/or motion data to the remote server computer, the motion data being generated by the motion tracker.
In one embodiment, pre-defined motion patterns stored at the remote server computer, and the system is configured
According to another embodiment of the surveying system, the motion tracker uses a machine-learning based algorithm for identifying motions and/or motion patterns, for instance including a Kalman filter.
According to another embodiment of the surveying system, the motion tracker is provided at the survey pole (e.g. integrated in or attached to it) and comprises the inertial measurement unit.
According to another embodiment of the surveying system, the IMU is integrated in the pole or attached to the pole. For instance, the inertial measurement unit may be integrated in the body of the pole or be part of a mobile device, wherein the pole comprises a receptacle for accepting the mobile device. For instance, the inertial measurement unit may be designed as a micro-electro-mechanical system and/or comprise at least three accelerometers in a mutually orthogonal configuration and at least three gyroscopes in a mutually orthogonal configuration.
According to some embodiments of the surveying system, the pre-defined motion patterns comprise a depositing motion pattern of the survey pole, in which the survey pole is moved from an upright position in a recumbent or reclined position.
In one embodiment, deriving motions and/or motion patterns comprises deriving the upright position of the survey pole and at least one of the recumbent or reclined position of the survey pole or a movement of the survey pole from the upright position to the recumbent or reclined position.
In another embodiment, the survey pole remains motionless or basically motionless in the recumbent or reclined position for at least a pre-defined time, e.g. at least five seconds.
In another embodiment, the action associated with the depositing motion pattern comprises switching off a GNSS unit of the survey pole and/or stopping tracking of a retroreflector of the survey pole by a surveying device of the surveying system.
According to some embodiments of the surveying system, the pre-defined motion patterns comprise a pick-up motion pattern of the survey pole, in which the survey pole is moved from a recumbent or reclined position in an upright position.
In one embodiment, deriving motions and/or motion patterns comprises deriving the recumbent or reclined position of the survey pole and at least one of the upright position of the survey pole or a movement of the survey pole from the recumbent or reclined position to the upright position.
In another embodiment, the survey pole remains motionless or basically motionless in the recumbent or reclined position for at least a pre-defined time, e.g. at least five seconds.
In another embodiment, the action associated with the pick-up motion pattern comprises switching on a GNSS antenna of the survey pole and/or performing a search by a surveying device of the surveying system for a retroreflector of the survey pole.
According to another embodiment, the pointer tip is configured for providing a punch or centre-punch functionality for physically marking a point on an object, e.g. for marking the measuring point. For this purpose, the pointer tip comprises a spring that is configured and arranged so that it is compressed when the pointer tip is pushed onto the point of the object and released when a predefined amount of compression has been reached. In this case, the defined motion patterns comprise a punch-motion pattern, in which the survey pole is pushed with the pointer tip onto a point of the object and the spring is released. In one embodiment, the action associated with the punch-motion pattern comprises performing a measurement for deriving the position of the marked point, e.g. the measuring point.
A second aspect pertains to a computer-implemented method for controlling a surveying system, such as the surveying system according to the first aspect. The surveying system comprises:
According to this aspect, the method comprises
A third aspect pertains to a computer programme product comprising programme code which is stored on a machine-readable medium, or being embodied by an electromagnetic wave comprising a programme code segment, and having computer-executable instructions for performing, when executed in a surveying system, e.g. in a surveying system according to the first aspect, the method according to the second aspect.
Aspects will be described in detail by referring to exemplary embodiments that are accompanied by figures, in which:
In both shown embodiments, the survey pole 10 comprises an inertial measurement unit (IMU) 18 placed on the body 13 with a defined spatial relationship relative to the position giving means, wherein the IMU 18 is designed in form of a micro-electro-mechanical system and comprises IMU-sensors including accelerometers and gyroscopes. The pole 10 comprises an evaluation unit 17 for deriving the position of the measuring point 1 at least based on the determined referenced position and on the defined spatial relationship of the position giving means relative to the tip 12.
The shown IMU 18 comprises three accelerometers in a mutually orthogonal configuration, i.e. in a configuration such that their measuring axes are orthogonal to each other, and three gyroscopes in a mutually orthogonal configuration, i.e. in a configuration such that their measuring axes are orthogonal to each other. Other possible setups that deliver high accuracies, special energy saving modes or higher update rates could include additional accelerometer and/or gyroscopes, e.g. with axes aligned in parallel to the before mentioned ones. Optionally, also a magnetometer may be included.
Although the IMU 18 in
Moreover, the evaluation unit 17, which are depicted in
Optionally, pointer tips may be provided at both ends of the pole 10, so that the retro-reflector means 11 or the GNSS receiver 19 are provided between the two tips (not shown here). Optionally, a pointer tip of the pole 10 may be configured to provide a punch or centre-punch functionality for marking of measuring points (not shown here).
The surveying device is referenced to a reference coordinate system and configured to measure a distance 2 and relative angles to the retroreflector 11 of the pole 10, so that a referenced position of the retroreflector 11 and, thus, the measuring point 1 can be derived.
In some embodiments, a remote data connection 5 between the surveying device 20 and the pole 10 or the control device 17 may be established, e.g. for providing measuring data to the operator 3 or for allowing remote control of the surveying device 20.
Often, as illustrated in
In
In some embodiments, the resting itself can be detected. For instance, detecting that the pole 10 rests statically against a wall or shoulder of the operator as shown in
Alternatively or additionally, the movements 41, 42 from the operating position to the resting positions on the ground and at the shoulder could be detected as motion patterns. For instance, such a movement 41, 42 and consequent lack of movement for the defined time can be detected based on the IMU data and interpreted as inactivity of the pole 10. As illustrated in
A detected inactivity of the pole 10 could be used to trigger an energy saving mode on the device. For instance, tilt compensation could be temporarily deactivated, or for a pole with GNSS, energy saving would disable real-time kinematic (RTK). Since processing RTK and/or tilt compensation requires a significant amount of processing power, it significantly consumes energy from the battery. A low-power motion state detection model that detects non-usage of the pole 10 or detects that the surveyor 3 moves between different measuring points, e.g. with a resolution of over 1 Hz, allows for a timely triggered energy saving. Preferably, in order to allow an effective energy saving, the continuously running low-power motion state detection consumes as few energy as possible. At the very least, it should consume significantly less energy than the units that can be powered down.
In the shown example, at the pole 10 a movement pattern 51 is detected in the picking-up movement 43 or by a repositioning movement 44 of the pole 10 from its resting position, e.g. laying on the ground where it is out of sight of the surveying device 20. This detected movement pattern 51 (e.g. on combination with the previously detected inactivity) is used for triggering a faster re-lock of the pole 10 by sending a command to the surveying device 20 via the remote data connection 5 to perform a certain action or workflow 52, e.g. a command to perform a search for the pole's reflective target near the last position or near a predicted position (e.g. predicted by means of dead reckoning).
Conventionally, when the line of sight between the prism 11 attached to the pole and the surveying device 20 is lost, the surveying device 20 will perform a search to find the pole 10, e.g. it will start rotating around the horizontal axis and/or the vertical axis searching for a prism 11. However, if the pole 10 is determined to be resting stationary (i.e. being inactive), this power search may be called off and delayed until the pole 10 is determined to be picked up again. If then a pre-defined pick-up motion 51 is detected, a command can be sent to the total station 20 to search near the last known position of the pole 10, narrowing down the search space drastically and, therefore, increasing re-lock speed significantly.
The inertial data 8 is provided to a motion tracker 40, i.e. a unit or device comprising a motion tracker algorithm. In some embodiments, the IMU 18 and the motion tracker 40 can be embodied as one unit that is provided at the pole. For instance, a field controller attached to the pole may comprise the IMU 18 and a motion tracker algorithm. The motion tracker 40 receives the inertial data 8 and derives, based thereon, actual motions and/or motion patterns of the survey pole.
A control unit, which may be embodied as a control and evaluation unit 17, has access to a database 48 which comprises a plurality of different pre-defined motion patterns of the survey pole, wherein each of the pre-defined motion patterns is associated with an action or workflow of the surveying system, or, more precisely, for one or more units or devices of the surveying system. Database 48 need not necessarily be understood as a database of motion patterns per se, but also can be seen a database of the features corresponding to the motion patterns or rather a database of mathematical functions describing the boundaries between different motion patterns in a projected (non-physical) space.
The motion patterns in the database may also include motion patterns which are used to minimize the error due to pole length, for instance if the target and the IMU are mounted between the pole tip and that part of the pole body that is held by the user, or in case of a pole having tips at both ends. If the pole has more than one pointer tip, e.g. one tip at both ends of the body, the motion patterns may also be used to determine which of the pointer tips is contacting the measurement point.
The motion patterns in the database may include motion patterns connected to inactivity of the pole 10, and the associated action or workflow of the surveying system may comprise powering-down units of the pole 10 or other devices of the surveying system, e.g. a total station. The motion patterns in the database may also include motion patterns connected to a re-activation, i.e. an end of inactivity, of the pole 10, and the associated action or workflow of the surveying system may comprise powering-up units of the pole 10 or other devices of the surveying system again.
The motion patterns in the database may also include motion patterns relevant for an inertial navigation algorithm, particularly if the inertial navigation algorithm is using the same IMU data. Practical examples may include zero velocity updates (no movement detected), upper/lower boundary (human step counting), or tuning of algorithm parameters related to motion (walking, running, driving, etc.).
In some embodiments, the motion tracker 40 and the control and evaluation unit 17 can be embodied as one unit, optionally also comprising the IMU 18. The control and evaluation unit 17 is configured to receive the motion data 4 and to determine, based thereon, whether a derived motion or motion pattern corresponds to one of the plurality of pre-defined motion patterns stored in the database 48. If a derived motion or motion pattern is determined to correspond to one of the pre-defined motion patterns, a command to perform the workflow 52-54 associated with this pre-defined motion pattern is sent to the respective unit(s) or device(s) of the surveying system, the workflow associated with the determined pre-defined motion state relating to a function of said unit(s) or device(s). In the shown example, these units and devices comprise a GNSS unit 19 of the pole, e.g. a GNSS antenna and a corresponding GNSS-processing unit, and a surveying device 20, e.g. a total station measuring distances and angles to a prism or similar target of the pole.
Optionally, the motion tracker 40 may receive more data than only the inertial data 8. As shown in
Optionally, the pointer tip is equipped with additional features having an impact on IMU measurements. An example of such a feature is a punch or centre-punch functionality, wherein the pointer tip comprises a pin, a spring and a mechanical guiding system. Such a functionality as part of a survey pole is known per se. Once the pointer tip having the punch or centre-punch functionality is pushed onto an object, e.g. onto a measuring point on the object, the spring is compressed. At a predefined amount of compression, the spring is immediately released by the mechanical guiding system, which causes the pin (e.g. made from hardened steel) to punch a notch into the object, in order to physically mark a point on the object, e.g. as a measuring point. This sequence of events may be related to a punch-motion pattern which is characteristic for the use of the punch or centre-punch functionality. The IMU detects accelerations, particularly those induced by the released spring, and the motion tracker identifies the accelerations as a motion pattern. Upon detecting this motion pattern, a predefined command is issued. For instance, this command may comprise automatically executing a measurement by the surveying system to determine the position of the physically marked point as the measuring point 1.
In
Preferably, IMU data 8 from a period of more than one second may be used for identifying a motion pattern or motion state, for instance IMU signals from about the last 1.5 seconds or from about the last 2 seconds. A Kalman filter state may be used for determining—using IMU data as well as GNSS data or data derived by the total station—attitude information and optionally also velocity.
One or several machine learning models (ML-models) such as Decision Trees, Random Forests or Support Vector Machines can be trained to classify the current movement into different categories. These models may run on the “edge”, i.e. directly at the pole 10 or in another unit or device of the surveying system (e.g. the control unit 17, or the surveying device 20 of
When an ML-model is used, the motion patterns 41-46 are recorded and used to train these models. The data is split into at least two datasets: Into a first dataset which will be used to train the model and into a second dataset which will be used to test and evaluate this model and all further models. The resulting model is then stored on device and available to the control unit 17, the database 48 of motion patterns is not needed on device but can be used for further re-training purposes. The motion patterns 41-46 comprise features extracted from the IMU signals, and optionally attitude and velocity information derived by total station, GNSS and/or IMU measurements which might be fused, e.g. in a Kalman filter. These features can be pre-defined statistics computed in the time and/or frequency domain over a signal of e.g. 1.5 seconds or 2 seconds length. These features are then input to a machine learning algorithm and used to classify the input signal into one or several of the predefined motion pattern classes. When a Neural Network is used, the network will learn and extract features on its own.
Using the pre-trained model(s), users can record additional motion patterns and configure workflows that will be triggered once this pattern is recognized on their own. Alternatively, users can re-record data for pre-defined motion patterns to re-train and individualize the models towards their needs. This is done by recording the motion pattern of the user on device and then either re-training offline (i.e. in the cloud or at a computer where the recorded data and a software would be loaded) or on the device directly. In both cases features would be calculated as before, and—using the previously trained ML-models and/or previously recorded motion patterns—a new training process would be triggered. Furthermore, new data recorded would automatically be split into a training and test dataset. The model would be trained using the new and optionally old training data. The new model can then be loaded onto the device. To assure quality of the model and evaluate its performance, automatic tests can be called to verify that the model's performance has not degraded (regression testing) by using recorded motion patterns from the old and new test datasets.
Recording of additional motion patterns could be achieved automatically. For this, user behaviour (e.g. initiating system actions or workflows by conventionally pressing a button) is logged in a specific user action log together with a certain amount of time-wise preceding IMU data in an accompanied IMU data log. The two logs are then sent to a repository where the logs of potentially many different users are evaluated to find typical user behaviour. This repository could be cloud-based or locally at the customer's site. Based on the method mentioned above, the database 48 of motion patterns 41-46 can be updated.
If one or more of the defined motion pattern are user-defined motion patterns, the actions associated with the user-defined motion patterns may include user-defined workflows of the surveying system.
When the definition process is started by a user, e.g. on a GUI of the control unit 17 or similar device, the control and evaluation unit determines the user-defined motion pattern based on motion data received during the definition process and associates this motion pattern to a workflow, e.g. a user-selected or user-defined workflow.
In some embodiments, detecting motion patterns is at least partially user-trainable. For instance, users may define and record motion data for their own motion patterns and pick from a menu which action or workflow to trigger. Alternatively, the user could record data for predefined motions to allow for better adaption or individualization towards the specific user (re-training or transfer learning). Moreover, user data may be gathered to train new models. For instance, the system might detect typical user behaviour and associated motion patterns. Corresponding data is then sent from the system to a central server, e.g. of the manufacturer. Based on this data, the catalog of motion patterns may be updated and distributed to all systems. It is also possible to run an anomaly detector which would only need a history of typical usage data (assumed to be “normal”) and would give rise to alerts when observing outliers.
In some embodiments, different motion patterns could be detected that enable different operating modes. For instance, the system may detect and differentiate between low-dynamic (“slow”), rather steady accelerations and movements (e.g. a pole fixed on a vehicle) vs. high-dynamic (“fast”), chaotic accelerations and movements (e.g. a surveyor carrying a pole around). Depending on the detected movement pattern, a different operating mode can be triggered for the GNSS module or total station, e.g. a high precision mode or a high lock-stability mode. Thus, with a modified sampling rate, a higher measurement precision can be achieved for more quiet movements. Or with a field-of-view adaption of the lock-sensor, the stability and robustness to keep the lock on high-dynamic targets can be improved. Furthermore, other sensors can be set to different sampling rates or field of views. Also, different operation modes could be automatically detected by running a classification algorithm on the IMU signal.
The described motions and motion patterns can be recognized either based on rules, using machine learning or using hybrid approaches.
Recognizing motions and motion patterns based on rules (rule-based) includes using a fixed, more or less manually defined set of rules (“conditions”) to identify certain states. An example for a rule-based approach is an energy-saving mode. In a specific case, this could comprise standstill detection or inactivity detection to save energy (e.g.: “if movement falls below threshold in 10s, then inactive”). These conditions can be defined in a data-driven manner and/or can be partially adjusted by the user (e.g. threshold values). The rules can define simple threshold values, comprise “if-then” conditions or be derived statistically.
Rule-based approaches only allow relatively simple rules and often need manual definitions. Using machine learning (ML), complex patterns or rules can also be recognized and learned. Specifically, a movement phase between two measuring points (i.e. the operator carrying the pole from one measuring point to another) can also be recognized and delimited from the actual measurement. This allows the use of an energy-saving mode even during movements. ML-based algorithms that can be used include decision trees, random forests, SVMs or neural networks. These algorithms can be used both for signal classification and for a detector. During the classification, the last measured values (e.g. two seconds) are used with a defined update frequency (e.g. 2 Hz) and classified by the algorithm. If necessary, a further step “feature extraction” takes place before the algorithm is called. A neural network or other “deep learning” methods learn the features independently; for classic algorithms, defined statistics in the frequency or time domain could be calculated here. Alternatively, the processing of the motion data is carried out in a rolling fashion by continuously processing a continuously generated time series of the motion data.
In the hybrid approach, rule-based approaches are combined with ML models. For example, simple states could be recognized rule-based for energy saving, more complex states using an ML approach. Examples for a hybrid approach comprise a decision tree with relevant situations and ML models running in each case, and, conversely, classification of the processes using ML and then a rule-based approach.
Although aspects are illustrated above, partly with reference to some preferred embodiments, it must be understood that numerous modifications and combinations of different features of the embodiments can be made. All of these modifications lie within the scope of the appended claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 21213394.6 | Dec 2021 | EP | regional |
