Currently, LiDAR is known as one of the most effective ways of mapping, thanks to its efficient acquisition of high-density and high-accuracy geo-referenced spatial data. 

In general terms, LiDAR is a remote sensing technology that measures distance by illuminating a target with laser. Then, the reflected light is analysed to produce high resolution 3D maps. 

In case of power line management, it is used to complete power line 3D visualization management and professional analyses. 


LiDAR technology allows technicians to know the real operation conditions of power lines and their capacity limitations. Thanks to this information, they can optimize infrastructure and ensure safety and reliability of power line systems. Additionally, with the combination of UAVs, they have no difficulties accessing and surveying even the roughest terrain quickly, cost effectively and without putting people at risk. 


Power line & utility infrared inspection services are designed to locate potential electrical problems and assist utilities with eliminating failures in transmission, distribution and substation systems. The use of aerial thermography cameras for power line infrared inspection provides the fastest and most accurate method of survey. Our aerial thermographers will be certified technicians, trained to interpret the complex data compiled during a typical survey. 


A light-weight aerial mounted camera system will be used on the drones. The aim is to source or design a camera that has a thermal resolution of 1/10°F, and a pictorial resolution of 525 lines per frame (560 CCIR) with 352 elements per line (428 CCIR). Current best-of-breed cameras utilizes a Mercury, Cadmium, Telluride (HgDcTe) 2×4 TDI detector array with an IR Special bandwidth of 8 – 12 microns. Such cameras render an image of an object’s thermal emissions in shades of black to white . . . white representing hot and black depicting cold. The data are then digitally recorded for processing. The camera is remotely controlled by the thermographer on the ground. LCRS rate stabilization assists the Thermographer in manual target tracking by dampening the effects of vibration and motion from the drone. 

Advantages of using aerial mounted infrared camera include: 

  1. Increased safety due to further distance from the lines than the typical hand held camera inspection or the use of manned helicopters 
  2. Optical zoom lens allows acquisition of high resolution close-up images. (not just a blown up digital image). 
  3. Mobility of the mounted unit and zoom lens means higher speed than a helicopter-based infrared survey out of the window. Flight speeds of up to 45 knots are targeted. 
  4. The ultra-accurate DGPS system enables the drone to fly far closer than a helicopter could, obtaining better resolution 

All these factors add up to an improved fault/fatigue detection than a typical hand held IR survey. 

A bolted clamp connection at the jumper. Analysis revealed a temperature rise of 52°C above conductor 


Our infrared (thermal) reports are concise and to the point, making anomaly location easy to determine and facilitate repairs. Your report will include: 

  • A detailed easy-to-read inspection report including: 
    • Written narrative of the survey 
    • Report form for every anomaly detected during the inspection 
    • Infrared DVD video of the entire inspection 
    • Hard copy and electronic version of the entire report 
    • 3D photogrammetry/LiDAR/Infrared/Thermal imaging layers 
  • A report sheet accompanies each problem which includes: 
    • Digital visible light photograph, color thermogram and black / white thermogram images 
    • Complete temperature analysis of the anomaly utilizing ambient temperature or conductor temperature for comparison 
    • GPS locations of the anomalies 
    • Infrared data 



The first proposed method to enhance flight safety is by means of initial high altitude fly overs. The range of this software and hardware configuration will be to allow at a maximum altitude of 500 m, with each flyover up to 1 km apart. 

The purpose of each fly over will be to collect photogrammetric data from specially fitted photogrammetric equipment attached to the base of the UAV/drone. At a height 500m, the photogrammetric sensors have a span of 500 m in either direction, providing a total span of 1 km.

The advantage of this approach is that is avoids the need for LiDAR equipment which will cost over $100,000 for 500 meter range and be too heavy for the drone. The photogrammetry data will be updated and improved with accuracy when the second LiDAR flyover is performed in the actual powerline observation take. The initial 500 meter fly =over process is illustrated in figure 5 below. 

Figure 5: Illustration of the High flyover photogrammetry process 

The photogrammetric technology involves processing and meshing a large number of visual surface images together creating a three-dimensional model of the underlying land and potential obstructions. 

The purpose of the three-dimensional model will be to provide the low altitude survey team with a detailed 3D model of the area of interest. The low altitude survey team will be able to study this three-dimensional model and identify all man-made structures and objects which would pose a collision threat before conducting the low altitude flying. 

Alternatively, this three-dimensional model could be integrated into flight simulation database allowing the pilots to conduct test flights before they embark each low level flight. 


The most important safety enhancement mechanism is our proposed flight planning solution. This solution has been proposed to manage all the risk featured in risk table 1.1. Prior to the commencement of any survey flight it is vital that the pilot fully understands the flight path – the terrain over which they will be flying, the man-made infrastructure which may be encountered during the flight as well as all the weather factors which may affect the safety of the drone and the equipment its carrying. The flight planning process will entail understanding and studying three sets of data. They are as follows: pre-existing GIS data, photogrammetric data collected from high altitude flyovers and local weather data measurements and predictions. 

The first two data sets, namely existing GIS data and photogrammetric data will serve multiple purposes in the safety enhancement process. Before the high-altitude surveying is conducted, it is of vital importance that the 

existing GIS data and current weather conditions are studied thoroughly by the pilot and the ground team (who will have the ability to communicate with the pilot). 

After the high-altitude surveying has been conducted, combining the photogrammetric data with the existing GIS data will enable us to create a virtual reality simulation which will be used to allow the pilot to view a simulation of the low altitude flight without risking his/her safety. 

Figure 6: UAV drone following a set path 

Finally, the photogrammetric data and the GIS data will be used to create virtual geo-fences which are essentially theoretical flight corridors which the UAV drone should fly within when performing the low altitude surveying. The geo-fences will be communicated to the pilot by means of a beeping system which will notify the pilot if he/she is flying off course. 


It is important that the aircraft remain in contact at all times. This for two principal reasons: The data being recorded by the aircraft’s surveillance equipment needs to be accurately logged to the specific locations in which it was recorded and this will be best done by a custom-built network positioning system. Secondly, should anything change that would affect the aircraft’s safety, such as the weather or physical obstructions (risks 1.1, 1.2, 1.3, 2.1 and 2.2), it is important that the pilot can control the drone, despite being in a region of low to zero network coverage. 


Whilst in the air, flying at low altitude for the highest accuracy of readings, the pilot will be largely reliant on his pre-flight planning and the cues given to him by the on-board geo-fence. This is important so that the pilot is not overloaded with information. There are two exceptions to this where other information will be provided to the pilot. Should anything change that only the ground crew is aware of, sudden weather changes for instance (risks 1.1, 1.2, 1.3), the pilot will be notified via the network. And secondly, an on-board LiDAR system will provide a final cautionary sensory warning. 

The UAV drone’s geo-fence is based largely on the input from the photogrammetry survey. However, should this survey have missed something, a telephone wire obstructing the UAV drone’s path for instance (risks (2.3 and 2.4), a forward facing LiDAR sensor will be able to provide a last minute warning 100m in advance. This will provide the pilot with an alert of an obstruction and a better opportunity to take evasive action. 

As well as the forward-facing LiDAR sensor, a downwards facing LiDAR sensor will be picking up detailed 3D data from the ground. This will provide a bare earth model of the ground surface and will update the existing photogrammetry 3D model. The outcome of this will be a highly accurate 3D model of the area of interest, which will of great value to all future surveillance projects. 


Foundations of LIDAR 

LIDAR means, Light detecting and ranging. 

LiDAR is an active remote sensing system. An active system means that the system itself generates energy – in this case, light – to measure things on the ground. In a LiDAR system, light is emitted from a rapidly firing laser. You can imagine light quickly strobing from a laser light source. This light travels to the ground and reflects off of things like buildings and tree branches. The reflected light energy then returns to the LiDAR sensor where it is recorded. 

A LiDAR system measures the time it takes for emitted light to travel to the ground and back. That time is used to calculate distance travelled. Distance travelled is then converted to elevation. These measurements are made using the key components of a lidar system including a GPS that identifies the X, Y, Z location of the light energy and an Internal Measurement Unit (IMU) that provides the orientation of the plane in the sky. 

Working of LIDAR Systems 

The principle behind LiDAR is really quite simple. Shine a small light at a surface and measure the time it takes to return to its source. When you shine a torch on a surface what you are actually seeing is the light being reflected and returning to your retina. Light travels very fast – about 300,000 kilometres per second, 186,000 miles per second or 0.3 metres per nanosecond so turning a light on appears to be instantaneous. Of course, it’s not! The equipment required to measure this needs to operate extremely fast. Only with the advancements in modern computing technology has this become possible. 

The actual calculation for measuring how far a returning light photon has travelled to and from an object is quite simple: 

Distance = (Speed of Light x Time of Flight) / 2 

The LiDAR instrument fires rapid pulses of laser light at a surface, some at up to 150,000 pulses per second. A sensor on the instrument measures the amount of time it takes for each pulse to bounce back. Light moves at a constant and known speed so the LiDAR instrument can calculate the distance between itself and the target with high accuracy. By repeating this in quick succession the instrument builds up a complex ‘map’ of the surface it is measuring. With airborne LiDAR other data must be collected to ensure accuracy. As the sensor is moving height, location and orientation of the instrument must be included to determine the position of the laser pulse at the time of sending and the time of return. This extra information is crucial to the data’s integrity. With ground-based LiDAR a single GPS location can be added for each location where the instrument is set up. 

Generally, there are two types of LiDAR detection methods. Direct energy detection, also known as incoherent, and Coherent detection. Coherent systems are best for Doppler or phase sensitive measurements and generally use Optical heterodyne detection. This allows them to operate at much lower power but has the expense of more complex transceiver requirements. In both types of LiDAR there are two main pulse models: micro pulse and high-energy systems. Micro pulse systems have developed as a result of more powerful computers with greater computational capabilities. These lasers are lower powered and are classed as ‘eye-safe’ allowing them to be used with little safety precautions. High energy systems are more commonly used for atmospheric research where they are often used for measuring a variety of atmospheric parameters such as the height, layering and density of clouds, cloud particles properties, temperature, pressure, wind, humidity and trace gas concentration. 

How this LIDAR system is suitable to this project 

When considering exactly what we need to achieve here, it is evident that the selected system, that being the drone and the lidar system fitted to this craft will be exactly what we need for this application. 

We have considered the accuracy, point density and the 3D models that this the system generates, and we are convinced that the system is 100% suitable for the purpose. 

See below some sample data from the proposed system 

Considering the picture above, it is evident that the power lines, continuity, and condition of the power lines are evident by the generated data. 

Additional benefits of this system 

There are additional benefits to this system, some of them are, but not limited to. 

The lidar system can be forward looking, this is for two reasons. The unit is small enough to be manoeuvrable on its gimbal as well as the fact that the drone has the capability of moving into very confined and tight spaces that you would need to do to have a forward-looking perspective. This could only traditionally have been achieved with a helicopter. Even with a helicopter, this would be considered to be fairly dangerous and would naturally not happen unless necessity dictates. 

With this drone we can do the inspection on the power lines from the point of view of a human, or his close as possible to that. This will then have the added benefit of analysing the daughter from a human perspective. The benefit of this being that psychologically faults will be easier to identify and analyse as humans are accustomed to this perspective. 

The resultant data that needs to be analysed is displayed below, the daughter speaks for itself in that it is very easy even for a person not engaged in the field to identify that the condition of the power lines in question here is continuous. 

Although can see some breaks in power line data, this is to be expected as not each and every point LIDAR will collide with the power line, we can fairly deduce that the power line is continuous as it is not hanging from either pillar. 

Adding thermal imaging to identify hotspots 

The use of thermal imaging is most definitely not a new innovation in power line inspection. We will incorporate this into this drone to substantiate the lidar data. The thermal imaging will be able to identify hotspots where there are bad connections due to damage to the power line or the conductor. 

The thermal data can be superimposed on the LIDAR data to generate a fantastic image of the exact condition of the power line and its associated equipment. 

Data delivery formats 

While the line or daughter will be deliverable in a 3D format as well as a 2D format, thermal daughter will only be deliverable in a 2D format. 

The LIDAR data is used to generate what is called a points cloud 3D model. An example below. 


The project comprises 3 milestones, where: 

Milestone 1 involves programming the flight of a quadcopter with visual-light camera, an infrared thermal imager and an ultraviolet imager, following a flight path generated by a previous fixed wing photogrammetry 3D dataset integrated into a relative positioning system and is divided into 4 milestones: 

Milestone 1A – integration of photogrammetry generated model with relative positioning system for flight planning 

Milestone 1B – bespoke development of a prototype quadcopter with visual-light camera, an infrared thermal imager and an ultraviolet imager with a target of 30 minutes of flight time and pixilation of camera images reduced to 1 millimetre accuracy when within 10 meters of powerline. 

Milestone 1C – integration of time of flight communications protocols for relative positioning system 

Milestone 1D – visualisation of camera data and over-ride functionality for operator 

Milestone 2 focuses on the development of autotracking algorithms and camera shooting angle management while collecting data and is divided into 4 milestones: 

Milestone 2A – develop auto-tracking algorithm to surround the powerline taking into account range of required corridor of 3D location uncertainty due to weather conditions 

Milestone 2B – Integrate airborne cameras’ shooting angle to be automatically modified to keep tracking and photographing power lines during the inspection replacing proprietary relative positioning system with traditional GPS co-ordinates 

Milestone 2C – temporal functionality to compare exact location of power lines relative to previous inspection to provide evidence of increased line tension and subsidence 

Milestone 2D – Assessment of three coordinate transformation systems three coordinate systems – the geodetic coordinate system, the geocentric rectangular coordinate system and the NEU coordinate system as well as the spherical approximation method of treating arc length as chord length are applied to this auto-tracking algorithm

Milestone 3 involves developing more effective software than is currently available to interpret data collected by the quadcopter and divided into 4 sub-milestones 

Milestone 3A – Assess effectiveness and develop software required to capture and process raw LiDAR, thermal imaging using spectral clustering techniques using Kmeans and Expectation Maximization (EM) algorithm to classify the pixels into the power lines and non-power lines, and using the findings for processing LiDAR inputs 

Milestone 3B – Development of software-based processing of parametric spectral clustering methods used in to automate the number of clusters using the Davies-Bouldin index (DBI) 

Milestone 3C – Develop spatial segmentation functionality within software using morphological and geometric operations, to eliminate the non-power line regions. 

Milestone 3D – Develop thermal imaging auto-analysis tool to highlight anomalies automatically as well as physical evidence of frying from visible spectra camera images 

These technology projects will enable Dronetech’s clients to complete a wider programme that will incorporate the following key activities:

  • Find the correct locations for application for TCP license and their conversion from TCP to full Exploration Licences; 
  • Remote Sensing work under the TCP desktop study phase as well as other remote sensing activities once converted to full Exploration Licences;
  • Highly structured and comprehensive airborne survey data collection and analysis;
  • Deployment of a grid of Passive Seismic seismometers to gather seismic data at a fraction of the cost of traditional 3D seismic;
  • Seismoelectrics and Tellurics;
  • Multi-client regional calibration activities utilising data collected across all 6 concession areas, as well as other data sources such as core samples and other geological data;
  • Geo-chemical Sampling across the concessions, incorporating a mixture of grid point and anomaly-based locations, as well as surface GeoChem and Gore-Sorber sampling (for identified key anomalies) and analysis of the results;
  • The consolidation of all exploration activity results across all concessions into a consolidated and calibrated results set that will enable the identification of the most promising drilling sites in order to increase the likelihood of a successful drilling outcome.
  • The undertaking of the actual exploration drilling at the identified locations.