News – UK Drone Show 2016

New Drones

DJI

The DJI stand held two of their new models.

The Mavic Pro is a portable system with collapsible arms allowing it to fit into a small backpack. Its’ FlightAutonomy technology allows obstacle avoidance and hover precision, while ActiveTrack allows the drone to follow the subject matter with a number of different shooting modes. It comes with a 3-axis gimbal and 4K camera.

It is available for pre-order for £1,099.

The Phantom 4 Pro is an upgrade of the Phantom 4 . It improves on a number of areas of the previous drone:

  • Improved camera with a 1-inch 20 megapixel sensor from a 1/2.3 inch 12.3 megapixel sensor on the Phantom 4.
  • Stereo vision sensors on the rear of the drone in addition to the front facing ones that were on the original.
  • New infrared sensors on the left and right of the drone.
DJI Phantom 4 Pro

DJI Phantom 4 Pro

The Phantom 4 Pro costs £1,589, while a version where the remote controller has an integrated screen costs £1,819.

Yuneec

A new combined thermal and RGB camera for the Typhoon H drone was available.

The camera costs £1,799.

New Technologies

A number of technologies under development were on view in the Innovation Zone.

Tetradrone

An interesting concept of combined drone and submersible, changing the type of propeller allows the drone to either fly or go underwater with the body being watertight.

www.tetradrones.co.uk

http://jgarnham94.wixsite.com/tetra

https://www.facebook.com/TetraDrones/?hc_ref=SEARCH

Tetra Drone

Tetra Drone

Available for funding soon on Kickstarter.

Seadrone

An innovative modular underwater drone by van Dijk FEM engineering B.V. with a camera and a crab module for removing material from the sea bed.

http://seadrone.nl/

https://www.facebook.com/SeaDroneNL

SeaDrone

SeaDrone

Droneball

Another innovative technology was droneball, which was a drone fully enclosed in a cage stabilizing system which protects the drone and its surroundings from damage as it flies around, it either fly or roll along the ground.

DroneBall

DroneBall

The droneball is getting launched on indiegogo on the 8th of December.

https://www.indiegogo.com/projects/droneball-the-bouncing-crash-resistant-drone-drones/coming_soon

 

 

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News – Flyt

The uses of computer vision technologies in controlling UAV flight have been mentioned in some blogs already and will be discussed in more detail in a future blog. It can provide autonomous means of flight which are difficult to replicate manually allowing the use of a UAV in agriculture, inspections, surveys, delivery or emergency response.

The means to implement computer vision technologies on UAVs has generally meant proprietary technology or a complicated open source route with the setup of hardware and the installation of a number of pieces of software before experimentation can even begin.

But the work of  Indian Navstick Labs has developed a number of products to solve these problems.

FlyTOS

FlyTOS operating system is an application development framework built upon Linux and ROS (Robot Operating System), meaning an integration with ROS modules/libraries and sensors. It also supports the APM and PX4 (Pixhawk) open source autopilot systems.

The systems allows the development of obstacle avoidance, autonomous landing with AR tags and object recognition, tracking and following. It’s object tracking can use simple OpenCV based algorithms to detect objects using color and shape and use a Kalman Filter for tracking. It can also incorporate OpenTLD for selecting objects in a display and then following them (this was originally published in MATLAB by Zdenek Kalal).

FlytConsole and FlytVision are inbuilt on-board web apps that aid on in the creation of applications.

It comes with  a web-based control station called FlyConsole and a 3D simulator called FlytSim.

The software can be downloaded for free and installed on an ODROID XU4 companion computer.

ODROID XU4

ODROID XU4

FlytPOD

The FlytPOD – Advanced Flight Computer is a companion computer system running the FlytOS system which is currently being funded as part of an Indiegogo project.

As well as coming with a suspended IMU for vibration damping and an external magnetometer it also supports RTK (Real Time Kinematics) GPS.

It’s USB3.0,  USB2.0, HDMI and user configurable I/Os connectors support a number of systems out-of-the-box including a Gimbal, PX4Flow (Optical Flow sensor), LiDAR (distance sensor) and USB Cameras. While the hardware interfaces on the FlytPOD support a number of specialized sensors including multi-spectral cameras, stereo cameras and LiDAR.

It is designed to be able to process photographs in the companion computer and stream them to ground.

The system comes in two models:

  1. FlytPOD Kit – uSD storage. It costs $499 ($399 in Indiegogo).
  2. FlytPOD PRO Kit –  This kit has the same features as the basic one but also offers sensor redundancy, with triple 3-axis Accelerometers and 3-axis Gyroscopes as well as double external Magnetometers, Barometers and External GPS. It also comes with the faster eMMC storage. It cost $799 ($699 on Indiegogo).

The Indiegogo funding ends on the 3rd October.

Increasing UAV Flight Time

One of the main problems with the use of UAV to record archaeology and cultural heritage is the amount of time that they can stay in the air. This is linked to the limitations of the current LiPo (Lithium-ion polymer) battery technology.

The increasing use of sensors and companion computers on-board also adds to the drain on batteries during flight.

In many cases it is solved by simply having a number of batteries, but of course this is costly with many of the smart batteries for commercial UAVs costing over £100 each. Larger systems can have up to 6 batteries on them allowing extended flight times but costing circa $1,200. With smaller systems this means that the survey needs to stop and start while the UAV lands and takes off adding time to a survey and requiring overlapping of the survey so that nothing is missed.

There are a number of developing technologies that can aid in this problem.

Battery Improvements

A number of developments in battery technology are being made driven by the technology industry, car industry and Formula E Racing that improve on the Lithium-ion technology that is currently employed.

Developments at the Pohang University of Science and Technology in South Korea could double the power of current Lithium-ion technology by combining porous stainless steel with thin-film electrolyte and electrodes. This would allow drones to fly for over an hour.

Among the new battery technologies being researched are Zinc-air and Lithium-air batteries which have a much greater energy density than Lithium-ion batteries. Lithium-air batteries use oxygen as the oxidiser, and will potentially be 1/5 of the price 4/5 of the weight and have 5 times more more charge than Lithium-ion batteries.

Aluminium- air batteries have allowed an electric car to drive 1,100 miles on a single charge (developed by the Phinergy company), because the metal is turned into aluminium hydroxide as the battery is drained the battery needs to be recycled after use.

Cables and recharge stations

Cables are one possibility that can potential provide limitless flight time, they could provide an ideal source of power for localised recording but are limited by the length of the cable which would limit mapping missions.

In May at the  NAB 2016 conference 3DR announced the release of the Hoverfly Tether for their Solo drone which has a 150 ft-long cable. The system plugs into standard electrical outlets.

3DR Hoverfly Tether

3DR Hoverfly Tether for Solo drone

For recording at a distance from the nearest power source a recharge station such as the DRONEBOX by HUS Unmanned Systems can be used, it provides a mobile generator for wireless charging a drone on a landing pad. While the REMOBOX is also available which is a tethered version of the system.

Dronebox

Dronebox

Hydrogen fuel cells

While their sister company, HES Energy Systems, is involved in the introduction of hydrogen fuel cells into UAV flight.

They can provide a significant saving in weight and increase in flight time, by increasing flight time to hours rather than minutes.

A number of manufacturers are developing the technology including the UK Intelligent Energy company whose work was discussed on an episode of BBCs Click.

Intelligent Energy Drone

Intelligent Energy Drone

In April the drone manufacturer MMC announced the first commercially available hydrogen-fueled drone – HyDrone 1800 – with a flight time of over 3 hours and a flight radius of 100 km.

HyDrone 1800

HyDrone 1800

Using existing mapping data to control UAV mapping flights – Part 1 – Preliminary Ideas and Experimentation

An intrinsic problem with photogrammetry is its requirement to keep the camera facing the subject matter. A much higher quality and more accurate 3D model is produced using the method than taking photographs at an oblique angle. This is especially true of buildings with with flat facades, (this has already been discussed in another blog).

Work has been done using computer vision to automate the control of the camera position so that it follows targets selected by the pilot. Although this has potential for some recording methods such as site tours, as discussed in another blog, it doesn’t aid in the recording of complex topography or architecture. Although there is potential for the recording of architectural elements using computer vision  technologies (this will be discussed in a later blog).

Other work is being done in using a low detail 3D model of a building to aid in the control of a UAV flying around it, but these are more aimed at collision avoidance than quality recording.

While in the future i plan to look at the potential pre-scanning a building with an aerial LiDAR scanner mounted on a drone before recording with UAV.

Potential solution

The camera gimbal of a UAV can be controlled both remotely and from the autopilot of the UAV which could be used to always keep the camera facing the subject matter, but without pertinent information this would have to be done manually. With wireless camera technology it is possible to remotely view what the camera is recording and so control the movement of the gimbal when required, but this would require a second person to control the camera while the UAV is being flown and would be difficult to implement effectively and costly in a commercial environment.

But it would seem to be possible to use existing 3D data of an area to control the flight of a UAV; both controlling the altitude and the angle that the camera gimbal is pointing. I have already discussed the use of DroneKit Python to create a UAV mapping flight, thish can also be used to control the angle of the camera gimbal.

Existing Data

There are a number of existing sources of data that can be used to aid in creating a mapping flight.

Within the UK LiDAR data is freely available at different spatial resolutions, much of the country is available down to 1m while other areas are available down to 0.25 m.

This resource through processing in GIS (Geographic Information System) software provides all of the information required to create a flight path over the area under study and to control the angle of the camera gimbal so that it will record it to a higher quality than before.

A digital elevation model (DEM) created using photogrammetry from existing overlapping aerial photographs can also be employed once it is georeferenced to its correct location. This resource may provide a higher spatial resolution than the LiDAR data and so a better resource for the creation of the flight path, but the landscape and structures may have changed since the photographs were taken causing problems (this can of course be a problem with the LiDAR data as well).

Co-ordinate system problems

One complication with using LiDAR data to control the UAV is the fact that it is in a different co-ordinate system than the GPS of the UAV (OSGB and WGS84). This can be solved be translating one set of data to the co-ordinate system of the other. As the number of points for the mission path will be a lot less than that for the LiDAR data it would make sense to convert the GPS data to OSGB, but this also requires that it be converted back after the flight path has been created added a certain amount of inaccuracy into the data as a conversion is never 100% accurate.

required Data

Three different pieces of data need to be derived from the LiDAR data which are required for the UAV mapping flight:

  • Altitude.
  • Slope.
  • Aspect.

The Altitude is contained within each point of the LiDAR data and is used when displaying the data in GIS software.

The Slope of the topography/buildings is measure in increments up to 90 degrees, with o degrees being flat and 90 degrees being a vertical face.

The Aspect is which way any slope is pointing in is measured in increments from 1-360 degrees. (degrees).

 

Slope_angles

Slope angles

Although it would be possible to create software that extracts the data from the LiDAR file while creating a flight path this is not currently an option. The flight path is currently created in a piece of software such as the Open Source ‘Mission Planner’ system. In this an area is chosen together with other variables and an optimal flight path is created. This flight path file can then be saved, it contains the X and Y co-ordinates of each point of the mission.

UAV Control

At its simplest the flight path can be created with the altitude and slope derived from the LiDAR being used to control both the UAV altitude and camera gimbal angle. This would work well for sloping topography but would be more complicated for areas with sharp breaks in slope (such as buildings).

Altitude Control

The altitude will need to be carefully controlled to make sure that the quality of the imaginary is consistent across the whole area under study. At its simplest this is easy to do using the altitude data within the LiDAR data, together with obstacle avoidance sensors to aid with safety.

The problem arises when needing to record something near or completely vertical. Rather than requiring a set altitude the UAV needs to maintain a set distance horizontally. This may be possible by creating a buffer in the data around steeply sloping areas.

Drone_flight_path

Problem with vertical offset

Camera Gimbal Control

Most low cost UAV systems come with a 2-axis gimbals, this means that the camera is stabilized so that it always stays in a horizontal plane but also that its rotation can be controlled downwards.

Gimbal_angle

The angle of the gimbal begins at 0 degrees for a forward pointing position to 90 degrees for a downwards facing position. This is how is its controlled within DroneKit.

As seen earlier the slope is calculated between 0 and 90 degrees for a slope.

There are two intrinsic problems with this method:

  1. The slope only goes between 0 and 90 degrees so there is no aspect data within it. If the drone camera is to be controlled to record the building as it flies over if needs to know which way the building is pointing as the 45 degrees on the left is not the same as the 45 degrees on the right. This could be solved by combining the information from the slope and aspect to give more detailed resulting data.
  2. Most standard gimbals are designed to only point forwards and downwards. This means that the UAV has to turn around to record the back side of the building or it needs to fly the path in reverse. The other solution is to use a UAV with a camera that can point in 360 degrees.

GIS Processing

A certain amount of processing is required within GIS software to get the required data from the LiDAR data and combine it with the required mapping flight path. For this ArcGIS has been used both due to its availability at university and my own familiarity with it.

Lidar

Considering the LiDAR data is for a specific square it makes sense to use raster data rather than the points and lines of vector data as it retains the accuracy of the data. The LiDAR data can be simply loaded into the GIS software as a raster.

Within GIS software the Aspect and Slope can be calculated and a raster created showing the results.

This can be done using the Spatial Analyst or 3D Analyst Toolboxes to provide Slope and Aspect rasters.

The data for these can be incorporated into Attribute Tables which can be exported into text files. It is possible to combine all of the data into one attribute table containing the Altitude, Slope and Aspect.

Although it is possible to export this whole raster file including all of the data it is not currently possible to automatically derive the data in software using the flight path, so a flight path has to be loaded into the GIS software.

Flight Path

The flight path file we created in the Mission Planner software needs to be loaded into a feature class in the GIS software. This can be done by loading the point data for the flight path into the software, this is the beginning and end point for each of the back and forth paths across the area needed to be recorded.

We next need to recreate the flight path using ‘point to line’.

Even though we have recreated the lines, deriving enough data from them is not possible as the flight path is designed to fly back and forth at a set altitude. For this reason we need to create a number of extra points. This can be done using ‘Construct points’ where points can be created at set intervals along a line. This can be linked to the level of data that is being used, so for this LiDAR data the points can be set at intervals of 1m.

Once this his has been done ‘Extract multi-values to points’ can be run on the 3 sets of data to create a table containing all of the required data for each point on the flight path we have created.

UAV Mission Creation

Now that we have all of the required input data for the UAV mapping flight we need to create the mission within Dronekit Python.

For the first level of experimentation we can just load the point data file into python then create a number of points for the UAV to fly to which give the X and Y co-ordinates and the required altitude. At the same time we can also program in the angle for the camera gimbal. It may be best to have the UAV hover at the positions for a second or two so that we know how the recording is going.

As already mentioned if we are only using a 2-axis gimbal we are going to have to have the UAV turn through 180 degrees to record the back sides of buildings and slopes sloping away from the camera. We should be able to do this by altering the UAV Yaw. We will need to have the Python read the aspect angle and change how it creates the flight path depending on the aspect of the slope/building.

Future Directions

ArcGIS allows the use of Python to run tools in its toolbox so it seems possible to create a python script which would automatically create a file with all of the information required from input files of LiDAR data and a flight path.

As QGIS also allows the use of python it would also seem possible to create the required file within this open-source solution.

 

UAV Building Facade Recording – Part 1 – Preliminary Ideas and Experimentation

The recording of buildings is an important area in Cultural Heritage, whether for conditional surveys or to record something that is about to be destroyed.

Traditional methods rely upon survey equipment such as Total Stations to take a number of points on the façade, but this results in only points and lines with no great surface detail.

Other more detailed survey techniques such as laser scanning and photogrammetry have also been employed. But laser scanning is expensive and both the techniques are generally ground based missing detail of the façade that is not visible from this position. Scaffolding or a cherry picker can be used to record the whole of the building but again this can add to the cost the recording.

Photogrammetry is a low cost method of producing high quality results but relies upon having the camera parallel to the building to produce the best results, as capturing photographs from an angle brings inaccuracies into the recording as well as there being more detail at the bottom of the 3D model created than at the top.

The UAV would seem to provide an ideal platform to carry a camera parallel to the building, recording photographs with the required photogrammetry overlap. And with its autopilot it would seem possible to automate the recording process allowing the mapping of the façade in the same way that the UAV  can map the ground.

There are of course a number of problems that need to be overcome.

Building Façade Recording

Manual

Building façade recording can be done manually with a UAV, but the larger and more complicated the building façade the mode difficult it is to do this accurately. As the pilot needs to control the UAV accurately in 3 dimensions as well as controlling its speed.

Although the results for an experimental UAV mission are acceptable the difficulty of maintaining a manual position can be seen in the image below.

Automatic

In order to automate the process you need to determine what parameters are required to record a building façade using photogrammetry.

These can be seen below.

Excel Calculations

Building facade recording parameters

First experimentation was done by taking the co-ordinates of the two ends of an example the wall from Google Earth (The south facing wall of the lay brothers’ quarters at Waverley Abbey in Surrey was used). These co-ordinates can then be used to determine the bearing that the wall lies upon and its width. Using the camera parameters and level of detail the required distance from the wall for the flight can be calculated using trigonometry. Trigonometry is once again used to calculate the offset positions for the left and right extent of the flight.

 

The image overlap can be used to determine the number of photographs required in the horizontal and vertical, and hence the change of altitude that is required for each flight pass of the building.

Calculate altitude changes

Calculate altitude

Although it is planned to have the ability for the UAV to hover and take photographs, it is much easier to have it take photographs as it flies across the building façade. This requires the additional calculations and control of optimum flight speed and shutter speed to take photographs which are not adversely effected by motion blur.

Shutter speed formula

Shutter speed formula

Shutter speed calculions

Shutter speed calculations

These preliminary calculations were done in Microsoft Excel.

DroneKit

The drone manufacturer 3DR provides a series of software development kits (SDKs) for writing applications to control your UAV using one of the open-source autopilot systems they support.

DroneKit Python uses the Python programming language and provides a number of examples to help with programming the flight of a UAV; these include flying from co-ordinate to co-ordinate up to complete missions. Together with this there is an API (application program interface) reference which provides all of the Python commands that can be used to control the UAV.

Python

Python is a fairly easy to learn programming language and as DroneKit already requires it to be installed and setup it makes sense to use the same language to calcuate the required paramaters for the flight path. This was done with the aid of a number of online resources. A graphical user interface (GUI) was created using the Tkinter Python package and was used to enter the data. The python code did the calculations then a file is exported which combines these calculations with the DroneKit code for controlling the autopilot. The final file when run will control the UAV flight.

Python GUI

Python GUI

Virtual Drone

Experimentation doesn’t need to be done with a live UAV, it can actually be done with a virtual one using a number of pieces of open-source software. These include Mission  Planner, ArduCopterMAVProxy and SITL (Software in the loop)

Virtual Drone

Virtual Drone

Next Steps

Experimentation with a UAV using the hardware and software is the next step to test whether a GPS can be used in close proximity to a structure.

Limitations of standard UAV GPS accuracy to within the range of meters also complicates the use of this method of controlling the flight. This either needs to be solved with the use of a more accurate GPS (although the proximity to the building may block the signal), sensors that measure distances or the use of computer vision technologies to control the UAV position. The UAV afterall currently only need to fly between two set points then at set altitudes above the ground.

DJI Phantom 4

The DJI Phantom 4 is the new model in the popular phantom range of quadcopters, it has a number of improvements over previous models.

DJI Phantom 4

DJI Phantom 4

Comparison of DJI Phantom 4 and 3

Model Phantom 4 Aircraft Phantom 3 Professional or Advanced Aircraft
Battery 4S 15.2V 5350mAh Intelligent Flight Battery 4S 15.2V 4480mAh Intelligent Flight Battery
Max Flight Time 28 mins About 23 mins
Vision Positioning System 10m 3m
Obstacle Sensing System Optical Sensor – 0.7 – 15m N/A
Intelligent Flight Modes Follow Me
Point of Interest
Waypoints
Course Lock
Home Lock
ActiveTrack
TapFly
Follow Me
Point of Interest
Waypoints
Course Lock
Home Lock

Using the TapFly mode you can tap on a position of the screen in the app to fly to that location.

One of its main improvements is the introduction of obstacle avoidance technology (Sense and Avoid) using cameras mounted above the legs on the front of the Phantom 4.
DJI Phantom 4 - Obstacle Avoidance

DJI Phantom 4 – Obstacle Avoidance

The system, and the subsequent technologies, rely on a companion computer within the drone attached to the various sensors which uses computer vision algorithms to detect obstacles in the drones path. Once it has detected an obstacle it will either hover or fly around it.

DJI Phantom 4 - Companion Computer

DJI Phantom 4 – Companion Computer

It also comes with an improved Vision Positioning System, for position hold without the aid of GPS, which raises the positioning altitude from 3m to up to 10m.

DJI Phanton 4 - Vision Positioning

DJI Phanton 4 – Vision Positioning

A final important new technology is ActiveTrack where a subject can be selected in the app, and once again using computer vision technologies, the Phantom 4 will follow the subject even when it is turning.

DJI Phantom 4 - Active Track

DJI Phantom 4 – Active Track

The DJI Phantom 4 is available for £1,229.00 and will be on general release from the 23rd of March. As such it will be the first commercially available drone with obstacle avoidance technology.

Benefits

The Phantom 4 provides a number of cutting edge technologies on a low cost platform. The benefit of ActiveTrack has already been discussed in a previous blog – UAVs for site tour recording – Part 1 – Theory while the potential of the sense and avoid and vision positioning system technologies will be discussed in a future blog on building recording.

Drawbacks

The main drawback of the system is the fact that the camera is not of the same quality as the Zenmsue X5 which is available for the DJ Inspire 1 Pro/Raw. But even this camera is not of the same specifications as many standard DSLR or mirrorless cameras, only providing 16MP.

News – 3DR and Sony UMC-R10C

3DR have announced the integration of the new Sony UMC-R10C Lens Style Camera into there Solo UAV platform. This will include a custom gimbal for the camera. It will replace the current GoPro camera with one of the quality of a DSLR camera capable of taking 20MP+ photographs.

The camera appears to be similar to the Sony ILCE-QX1 discussed in a previous blog – Mirror-less Cameras and UAVs. Although the technology had great potential for use in UAVs due to the fact it does not have the body and weight of a normal camera, it had the serious limitation of not having a full manual mode. Hopefully this will be remedied in the new model.

Sony UMC-R10C

Sony UMC-R10C

The UMC-R10C is going to be released by 3DR as part of a complete mapping and processing solution called SiteScan.

It is not currently known whether the camera and gimbal will be available separately at present.

The camera will be unveiled at the NAB (National Association of Broadcasters) Show in Las Vegas in April.

While the package combined with SiteScan is expected to ship in June.