Choosing a Foreign Online Casino
A foreign online casino is a site that allows players to play a wide variety of games. These can include online poker, bingo and other card games. They offer a great range of options, but players need to be aware of the local laws in their home country before entering.
Some countries, such as Thailand and Indonesia, have banned all forms of gambling. Others, such as Argentina and https://casinosistersites.info/yebo-affiliates/punt-casino-sister-sites/ Costa Rica, have legalized it. Countries such as Finland, Brazil and the Dominican Republic have not punished off-shore gambling. The law regulating online gambling in these countries is fairly loose.
The most important thing to know about playing at an international online casino is that it will be different from what you’re used to. You’ll also be able to choose between a variety of banking options. However, some countries may have their own restrictions on what currencies are accepted, and you may have to move your money from one bank to another to play in a different currency. Fortunately, many international online casinos accept PayPal, so you don’t have to worry about this.
Another important factor to consider when choosing an international casino is the type of bonuses and promotions offered. Many of the top international online casinos offer lucrative welcome bonuses to US-based gamblers. It’s not uncommon to find an online casino that offers a free spin on a slot machine, or to find out about a weekly slot machine news roundup.
One of the most popular ways to deposit and withdraw funds at an online casino is through an e-wallet. E-wallets allow players to transfer their money securely and anonymously. Most of the best international online casinos accept PayPal in their cashier sections, which is a boon for a variety of players.
A small, but nonetheless impressive number of international casino sites are available to US residents. For example, some Curacao-based RTG-powered casinos offer generous welcome bonuses to US-based gamblers. Although there are no US-specific rules, some provinces still regulate the activity, so be sure to check with your local authorities before logging on to an overseas site.
Another good reason to choose an international casino is that they usually have a wider selection of games. While you’ll likely find some of your favorite slot machines, you’ll also be able to try your hand at table games and other variations. Also, some of the most popular international online casinos offer real-money gambling. This makes for an authentic internet gambling experience.
One of the most important things to keep in mind when playing at an international site is the type of security measures the casino uses. The best online casinos have a range of encryption technologies, such as Secure Sockets Layer (SSL), which protect your financial data. In addition, they often have a secure payment processor, such as Paypal or Skrill.
There are no laws specifically regulating online gambling in Lebanon. In fact, the Casino du Liban’s license is set to expire in the near future. Thankfully, there are some alternative options, including brick-and-mortar casinos in Beirut. With the right tools, it’s not hard to find an excellent international online casino that’s perfect for you.
Types of Gambling Games
Gambling games are played with cards, dice, and even the lottery. They can be very exciting and can be very dangerous. However, it is important to play gambling responsibly. There are a variety of different types of gambling games, and it is important to know the rules of the games you are playing.
Poker, slots, and pai-gow poker are the three most common casino games. These games can be played online or at a brick-and-mortar casino. Each of these games can be played with real money or with fake money.
Most casinos are open to everyone, and offer games for all ages. Some are strictly for older people and require some degree of skill, while others are designed for kids.
Depending on the type of gambling game you are playing, there may be a need for a Game Master. A Game Master is a person who selects which bets to make and monitors the results. Many casino games involve a revolving wheel, where players place bets on different sections of the wheel. This method is usually used https://boosty.to/davidhufersen/posts/7ca08db4-b5df-4493-b188-8c283e4035db to allow for a more realistic experience for younger players.
Some games, such as poker and blackjack, require a certain amount of skill and knowledge to play correctly. However, most gambling games are for entertainment only. The odds of winning a particular game are low, but there is always the chance to win something.
The most popular gambling game is the slot machine. It’s a popular game for both young and old. The newer versions have more advanced features and a better user interface. If you like a particular game, it’s worth checking out the demo version to get a feel for the sivustot game.
A lot of casinos offer bingo and other games that don’t require real money. They are a lot of fun, and you can often win some extra cash for a few hours of playing.
Another popular game is the Texas Hold’em poker game. In this game, players pay to join a league, and if they are the winner of that league, they earn money.
A social gambling game is one that is tied into your Facebook account. In this game, you can compete with friends, and the winner wins a prize. You can also earn extra coins by accumulating the points awarded for each win.
The main draw of a gambling game is the opportunity to win. Typically, this means that you will win money, but it doesn’t necessarily mean that you’ll be able to keep that money. Since the odds of losing are high, you’ll need to play well to avoid losing money.
One of the most popular forms of gambling today is the state-operated lotteries, which can be found in a number of countries. Often, these lotteries are organized by a commercial company. While it’s legal to participate in these lotteries in many countries, it’s not always possible to wager on an event.
Unlike most casino games, there are no strict rules to follow when playing a social gambling game. However, it’s best to make sure the environment is fun and not dull.
How to Get Around GamStop
Getting around GamStop is not a straightforward process. GamStop is a registration scheme for gambling sites that is designed to prevent problem gamblers from getting into trouble. Gambling sites that use GamStop will refuse to allow you to use their services. If you want to be allowed to gamble, you must sign up with a different casino or betting site. You also cannot use your own payment method. This means you can’t use the credit card you used to pay for your last gaming session.
The first step in getting around GamStop is to change your personal information. You must use different letters and punctuation when typing your name on the sign up form. This will help you to avoid getting a source gambling ban. You can also choose a longer self-exclusion period. After the period has expired, you can resume your gambling activities.
Another method to get around GamStop is to use someone else’s details. If you are a problem gambler, you can ask your friends or family to register with a gambling site. But you must be very careful when using someone else’s details. This could be a serious problem. It’s not a crime, but you will not be able to use the same payment methods as the other person. You should also avoid using someone else’s name and IP address. This could get you into trouble with the UK Gambling Commission.
There are some legitimate gambling sites that are not a part of the GamStop scheme. These include online bookies and poker rooms. They also offer attractive reward programs and a wide game selection. They also feature robust security and payment providers. Unlike GamStop, these sites don’t require you to submit KYC information. You can also use these sites to play with bitcoins. These sites are not part of the GamStop program, but you can still enjoy the same games and get great bonuses.
Gambling sites that don’t participate in the GamStop program also don’t offer you self-exclusion. However, they do offer better payment methods. They also feature higher payout rates. You may be tempted to bet with these sites instead of using the ones that are part of the GamStop program. You should only do this if you feel your situation has changed. It may also help you to get a better handle on your gambling problems.
Getting around GamStop is not as difficult as you might think. You need to shop around and choose a gaming destination that is reliable and trustworthy. You should also consider bonus offers and licensing. You can also ask your service provider to remove the block.
You also need to wait for the specified period to pass before you can remove yourself from the GamStop program. You can choose to go on a self-exclusion period for up to six months, or even a year. The self-exclusion period can be extended, but you will not be allowed to play for another month.
We’ve done it!
Imagine if you could drive to a car park and then, instead of spending 20 mins driving around trying to find a place to park, you tell your car to park itself. The car goes off and finds a spot. When you’re ready you summon it to come and pick you up. A bit like valet parking, except your valet is always with you when you drive.
2.5 years ago Parkopedia, the University of Surrey and the Connected Places Catapult set out to answer a question; how will autonomous vehicles park? We knew that this would be a difficult problem. Parking in a multi-storey car park means that there is no line of sight to satellites, which means no GPS. That said, GNSS (even RTK-GNSS) is not accurate in urban environments due to the canyon effect. As a result, a vehicle would need to estimate where it is using on-board sensors such as cameras, inertial measurement units and wheel odometry. We know that it’s possible to localise using LiDAR if a point cloud of the environment is available but LiDAR systems were very expensive. We chose therefore to focus on visual-inertial localisation.
Our goal was to work out what maps would be required to support the autonomous vehicle. We set out to develop the localisation and navigation algorithms that best utilise these maps and to prove them on our own autonomous vehicle. We had to ensure that any autonomous driving was done safely. Finally we wanted to find out what you, the general public, think of the idea of your car parking itself.
The project scope was defined by these 5 objectives:
- Developing automotive-grade indoor parking maps required for autonomous vehicles to localise and navigate within a multi-storey car park.
- Developing the associated localisation algorithms – targeting a minimal sensor set of cameras, ultrasonic sensors and inertial measurement units – that make best use of these maps.
- Demonstrating this self-parking technology in a variety of car parks.
- Developing the safety case and prepare for in-car-park trials.
- Engaging with stakeholders to evaluate perceptions around AVP technology
After the 7th quarterly meeting we published an update on the current status that showed that most of the work was complete. All that was left was to deploy the localisation and navigation software onto our autonomous StreetDrone test vehicle.
In the video above the software plans a route from the drop-off zone to the selected target parking spot when the driver presses ‘PARK’. Next, the vehicle localises itself by estimating its position with respect to the Artificial Landmarks. A recursive Bayesian Filter fuses the observations with odometry information . Finally, the software plans a path back to the pick-up zone when summoned.
With that demonstration we have completed all the deliverables for the AVP project and have successfully brought the project to completion on time and on budget! Our thanks go to
- Innovate UK and the Centre for Connected and Autonomous Vehicles for funding the project
- Admiral for insuring our StreetDrone for the duration of this project
- National Car Parks for allowing us to test at NCP London Bridge
Throughout this project we learned that drivers value the convenience promised by cars that will park themselves. This project has delivered a major breakthrough by identifying and overcoming obstacles to full deployment of AVP through the development of a technology demonstrator. At some point in the next few years your car will be able to park itself and we’ll be proud to have played a role in making that happen!
Two years ago Parkopedia joined the Autoware Foundation as a founding member. With that short space of time all the necessary software for a full-scale AVP demo has been developed and tested. Automated Valet Parking (AVP) is the ability of a vehicle to park itself and to return to the driver when summoned.
The Autoware AVP was a complete success! Parkopedia is proud to have supplied the map of the AutonomouStuff car park in San Jose, CA where the demonstration took place. Parkopedia also supplied the path planning software that plans the route the vehicle will travel. In this case from the drop-off zone to the desired parking spot and back to the pick-up zone.
This is further confirmation that Parkopedia’s high definition digital maps are indeed suitable for navigation of automated vehicles inside car parks.
As Dejan Pangercic writes; “The demonstration itself was the culmination of a week of intense effort from contributors around the world, working around the clock to put the finishing touches on the low-speed maneuvers needed to accomplish the park and retrieve functions. The flawless performance highlights the engineering quality built into the ground-up design of the open-source Autoware.Auto software stack, intended to serve as a strong starting point for companies developing autonomous systems for commercial, mission-critical, or safety-critical uses.”
Congratulations to the entire Autoware team for all the hard work that has paid off handsomely!
The Autonomous Valet Parking (AVP) project is a 30 month project funded by InnovateUK and the Centre for Connected and Autonomous Vehicles. It is scheduled to finish on 31 October 2020.
With more than three-quarters of the project timeline having passed, we are proud to say that we feel on track to achieve the project objectives within the allocated project time allowance, even with the current additional challenges of COVID-19 self-isolation restrictions.
Over the 5 objectives, the current status is as follows:
- Develop automotive-grade indoor parking maps required for autonomous vehicles to localise and navigate within a multi-storey car park. Parkopedia believes this map-based approach is the best way to achieve global scale for the roll-out of an Automated Valet Parking feature, which is likely to be the first SAE level 4 feature. This goal is 100% achieved, as Parkopedia has collected data in a number of car parks around Europe and is creating an inventory of maps to be able to supply to customers. Some of these maps have been made available to the research community under a Creative Commons license.
- Develop the associated localisation algorithms, targeting a minimal sensor set of cameras, ultrasonic sensors and inertial measurement units, that make best use of these maps. We have agreed to use Artificial Landmarks in the final demo for the project, and toward that effort this goal is 95% complete. Details about Artificial Landmarks and how they can be used for localisation are available in this blog post. For the remainder of the project the research effort will be directed towards localising with natural landmarks which is a much more difficult problem.
- Demonstrate this self-parking technology in a variety of car parks. This is well underway, and the outstanding work items now exclusively relate to integration with the map and localisation algorithm. Great care is taken to account for car park ramps, as by necessity, the low concrete walls are at their closest to the car at this moment. These ramps are considered to be the point of greatest risk as the localisation methods have to work extra hard when moving between floor levels. Also, the vehicle control algorithms need to account for gravitational acceleration of the car down the slope, and slowing it on the up-slope. After a lot of testing in simulation and more than 250 hours of in-car testing, we are pleased to have overcome this challenge, which you can see in action in the video below.
- Develop the safety case and prepare for in-car-park trials. Safety documents to cover the testing thus far have been published. A final document to cover demonstrations with large numbers of people is the last item outstanding. We have secured initial agreement for a final demonstration in a different car park to showcase the functionality.
- Engage with stakeholders to evaluate perceptions around AVP technology. We have engaged with the wider public around this technology and the results have been published.
The project recently held the 7th (of 10) quarterly review meetings where demonstrated the vehicle’s capabilities to the project steering committee and stakeholders.
The outstanding work items now exclusively relate to integration with the map and localisation algorithm, but we are confident of completing the project on time with our objective achieved. We are looking forward to the day this feature is available in a production vehicle!
Localisation is a central problem in robotics and it is very relevant to the AVP project. A self-driving car that is looking for an empty parking slot must know where it is on a map. For a precise manoeuvre, such as parking, an equally precise map and localisation algorithm are required.
The AVP project also has to respect a realistic budget for sensors, which rules out LiDARs in favour of cameras and IMUs. For this reason the project is committed to develop a vision-based localisation solution that uses HD Maps. Vision-based localisation however is very difficult and no one has yet demonstrated a system that works accurately and robustly in a fully general environment.
Within the International Standards Organisation, Technical Committee 204, Working Group 14, Parkopedia is part of a drafting team that is developing a standard for Automated Valet Parking Systems. The drafting team has agreed on the requirement for Artificial Landmarks, i.e. fiducial markers to be manually positioned in a carpark to enable accurate, robust localisation. At minimum, artificial landmarks are necessary around the pick-up and drop-off and zones to initialise the localisation system of a vehicle equipped with AVPS.
The next section will give an overview of localisation with landmarks.
Background to localisation with landmarks
The first step of localisation with landmarks consists of detecting the landmark with the available sensors. In this example we are using a camera, so we need to find the pixel coordinates of a landmark in an image. Note that we use interchangeably the terms landmark and marker.
The second step is to estimate the sensor position with respect to the landmark. In the case of a calibrated camera and of a marker with known size, a single image is sufficient to estimate the rigid transformation between the camera and the marker. The algorithm used is a variation of Perspective-n-Point.
The third and last step is to estimate the pose of the camera in map frame. We know the position of the camera with respect to the marker from step 2. Provided that the marker is distinctively identifiable, we can find its position in the map. By chaining the two transformations, we obtain the desired pose of the camera in map frame.
There are many designs for artificial landmarks in literature.
Given the localisation process outlined in the previous section, we know some requirements for a good landmark. It must be easy to detect to facilitate the first step. It must be distinctive enough to be told apart from the other landmarks. And finally it has to be of a size and shape easy to handle in practice.
There is a convergence to a black and white square shape marker because its properties:
- High contrast
- Simple geometry
- Easy to encode information
High contrast and square shape are clearly useful in detection because they can be exploited by established computer vision techniques. For example thresholding or line detection.
The information encoding part has more degrees of freedom that can be used differently, keeping in mind the goal of having highly distinguishable landmarks.
The basis of all the approaches is to subdivide the marker in a grid of small squares and use a binary encoding. That is to assign a power of two to each square, that is selected or not based on whether the square is black of white. By using a 2×2 grid we can represent 16 values as shown in the image below.
If we were to use all the possible markers for a given grid size, we would have the problem of erroneous detections. Some markers are very similar and minor occlusions or image noise could confuse the detection process. There are mainly two ways to deal with this problem, both of them based on the idea of sacrificing some information to achieve greater safety.
The first of these strategies is to set some squares to error detection. These squares convey no information on the id of the marker, but act as a necessary condition for correctness. The use of parity bits is widely studied in information theory and different schemes are available with different properties.
The second strategy is to maximize entropy, that is the distance between markers. Intuitively two markers with no square in common are far, while a marker is maximally close to himself. This notion is formalized by the concept of Hamming distance, which is also a widely studied topic in information theory.
The next two sections will analyze two marker types: ArUco and the standard proposed by the ISO drafting team.
ArUco markers are a state of the art fiducial marker system explicitly designed for localisation.
The information encoding is designed for optimal intramarker distance. Possible markers are iteratively sampled and only the ones with a sufficiently large Hamming distance are selected.
ArUco is very flexible as it provides multiple dictionaries with different sizes. The authors of the paper provide a production grade implementation in OpenCV that also has Artificial Reality capabilities, very useful for debugging.
The following code snippet is an example of the use of a the Aruco library for localisation.
// Retrieve image of environment with Aruco marker
cv::Mat input_image = /* retrieve image from camera */;
// Initialise predefined dictionary DICT_4X4_250
auto dictionary = cv::aruco::getPredefinedDictionary(DICT_4X4_250);
// Initialize MarkerVector struct, output parameter of the detection function
// Detect markers in the image
cv::aruco::detectMarkers(input_image, dictionary, markers.corners, markers.ids);
// Initialize camera intrinsics
cv::Mat K = /* camera matrix */;
cv::Mat D = /* distortion coefficients */;
// Set marker size
float marker_size = /* marker size including black border */
// Estimate marker pose
cv::aruco::estimatePoseSingleMarkers(markers.corners, marker_size, K, D,markers.rotations, markers.translations);
We provide a downloadable with the PDF version of the ArUco dictionary DICT_4X4_250. An A2 version, more suitable for printing, is also provided for convenience. It is common to print the markers on waterproof PVC and mount them on 3mm plastic or aluminum.
The AVPS drafting team has chosen to use a custom definition for artificial landmarks that explicitly encodes the orientation, data bits and parity bits for error checking. This encoding can be seen in the image below. Rotation is encoded through the four corner squares, with the top left white and remaining 3 black. With the orientation fixed, 8 data and 4 parity bits are encoded with the remaining 12 bits to create a Hamming Code.
It is possible to create custom dictionaries in OpenCV to use with the ArUco library. We have encoded the ISO dictionary as a custom one in a single header file which you can just include in your software.
Only one line of code from the previous example has to change – the creation of the dictionary – then the same code can be used to detect ISO markers.
auto dictionary = cv::makePtr<cv::aruco::Dictionary>(iso::generateISODictionary());
By using the library in this straightforward way, we see a lot of false positives, because we are not using the error correcting properties of the Hamming codes. A first step to improve the situation is to set the detector parameter errorCorrectionRate to zero, to disable the default correction done by ArUco. A better solution, that uses the full potential of the Hamming codes, requires to modify the ArUco detection algorithm.
A reasonable question to be asking right now is whether all this information above is even necessary. Can we not enable localisation without artificial landmarks?
It turns out that this is a difficult problem and industry is looking for a solution. The University of Surrey is developing a vision-based localisation algorithm that avoids the use of Artificial Landmarks as part of the AVP project and we look forward to demonstrate this technology on the AVP StreetDrone.
Expect to see a StreetDrone parking itself autonomously soon!
We are delighted to be publishing today the Safety Documents created to support the safe testing of the Automated Valet Parking function using the StreetDrone test vehicle in a car park.
This is the result of months of effort by Connected Places Catapult and Parkopedia. Following the post on Systems Engineering these documents now support the case made to drive under autonomous control within a car park.
These safety documents encompass system safety and operational safety.
– System safety:
- Safety Plan
- Safety Case
- Failure Mode and Effects Analysis (FMEA)
- Hazard and Risk Assessment (HARA)
- Trials Plan
– Operational Safety:
- Risk Assessment and Method Statement (RAMS)
- Report on testing in a controlled environment
The Safety Plan and the Requirements are live documents (they have been updated for testing in a car park and will be updated for future milestones)
Any entity seeking to conduct autonomous vehicle trials will need to develop and publish a safety case specific to their own trials (as specified by the government’s Centre for Connected & Autonomous Vehicles (CCAV) Code of Practice for Automated Vehicle Trialling) and gain permission to do so.
One of the main objectives of the AVP project is to create maps of car parks. Parkopedia is committed to working with our Open Source partners through the Autoware Foundation and have therefore released 3 maps of car parks to the community under the Creative Commons 4.0 BY-SA-NC license.
The maps are designed to be machine readable and are supplied in the OpenStreetMap XML format. This format is widely used and forms the basis for the OpenStreetMap mapsthat anyone can contribute to using tools such as the Java Open Street Map editor.
Our maps are designed to be useful for Automated Driving, which is why we’ve decided to make use of the Lanelet library as the data model for maps within the Autonomous Valet Parking prototype vehicle.
You can download the maps here and the following code can be used to plan a path using the lanelet library.
# libs import lanelet2 import lanelet2.core as lncore # load the map, for example autonomoustuff osm_path = os.path.join(os.path.dirname(os.path.abspath('')), "AutonomouStuff_20191119_134123.osm") print("using OSM: %s (exists? %s)" % (osm_path, os.path.exists(osm_path))) # load map from origin lorigin = lanelet2.io.Origin(37.3823636, -121.9091568, 0.0) lmap = lanelet2.io.load(osm_path, lorigin) # ... and traffic rules (Germany is the sole location, for now) trafficRules = lanelet2.traffic_rules.create(lanelet2.traffic_rules.Locations.Germany, lanelet2.traffic_rules.Participants.Vehicle) graph = lanelet2.routing.RoutingGraph(lmap, trafficRules) # create routing graph, and select start lanelet and end lanelet for the shortest Path startLane = lmap.laneletLayer # lanelet IDs endLane = lmap.laneletLayer rt = graph.getRoute(startLane, endLane) if rt is None: print("error: no route was calculated") else: sp = rt.shortestPath() if sp is None: print ("error: no shortest path was calculated") else: print [l.id for l in sp.getRemainingLane(startLane)] if sp else None # save the path in another OSM map with a special tag to highlight it if sp: for llet in sp.getRemainingLane(startLane): lmap.laneletLayer[llet.id].attributes["shortestPath"] = "True" projector = lanelet2.projection.MercatorProjector(lorigin) sp_path = os.path.join(os.path.dirname(osm_path), os.path.basename(osm_path).split(".") + "_shortestpath.osm") lanelet2.io.write(sp_path, lmap, projector) # now display in JOSM both, and you can see the path generated over the JOSM map # Ctrl+F --> type:relation "type"="lanelet" "shortestPath"="True" # and the path will be highlighted as the image below
Happy path planning!