Category: Software

Self driving car software

Localisation with Artificial Landmarks


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.

Artificial Landmarks

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

 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 
MarkerVector markers;
// 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.

ISO markers

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.

As in the case of ArUco, we provide PDFs of the ISO dictionary as 30×30 cm squares and in A2 format.


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!

Path planning using the OSM XML map

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 =, -121.9091568, 0.0)
lmap =, 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[2797] # lanelet IDs
endLane = lmap.laneletLayer[2938]
rt = graph.getRoute(startLane, endLane)
if rt is None:
    print("error: no route was calculated")
    sp = rt.shortestPath()
    if sp is None:
        print ("error: no shortest path was calculated")
        print [ 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[].attributes["shortestPath"] = "True"
    projector = lanelet2.projection.MercatorProjector(lorigin)
    sp_path = os.path.join(os.path.dirname(osm_path), os.path.basename(osm_path).split(".")[0] + "_shortestpath.osm"), 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!

Autoware workshop @ Intelligent Vehicles Symposium 2019

IV2019 brings together researchers and practitioners from universities, industry and government agencies worldwide to share and discuss the latest advances in theory and technology related to intelligent vehicles.

The Autoware foundation is hosting a workshop on Sunday 9th June 2019, with the aim of discussing the current state of development of Autoware AI and Autoware Auto, and considering various technical directions that the Foundation is looking to pursue. Parkopedia’s contribution is around maps and specifically, the integration of indoor maps for Autonomous Valet Parking.

Parkopedia’s Angelo Mastroberardino will be presenting our work on maps, answering questions like “Why do we need these maps?”, “How do we represent geometry and road markings within maps?”, and naturally leading towards the question of how we use these maps for path planning within indoor car parks.

Later, Dr Brian Holt will be joining Tier 4, Apex.AI, Open Robotics, TRI-AD, and Intel on a panel to discuss Autoware and its impact on autonomous driving.

Parkopedia joined the Autoware Foundation as a premium founding member, because we believe in open source as a force multiplier to build amazing software. We are contributing maps, including for the AutonomouStuff car park in San Jose, USA, which you can download for use with your own self-driving car in simulation. Find out more


Parkopedia’s mission is to improve the world by delivering innovative parking solutions. Our expertise lies within the parking and automotive industries, where we have developed a solid reputation as the leading global provider of high quality off-street and on-street parking services.

Parkopedia helped found the AVP consortium because we believe that Autonomous Valet Parking will become an important way in which we can serve our customers, by reducing the hassle of the parking experience. Parkopedia are providing highly detailed mapping data for off-street car parks, one of the critical components to a car being able to successfully park autonomously.

To make Autonomous Valet Parking a reality, the consortium first selected the StreetDrone.ONE as its car development platform. We are now developing the software stack to run on our StreetDrone with NVIDIA Drive PX2. The University of Surrey, another founding member of the AVP consortium, provides the camera-based localisation algorithms needed for the car to navigate autonomously inside a parking garage, which will support vision, in addition to LiDAR-based localisation.

Parkopedia has joined the Autoware Foundation as a premium founding member, along with StreetDrone, Linaro/96Boards, LG, ARM, Huawei and others. We believe in open source as a force multiplier to build amazing software, and the AVP consortium is committed to using Autoware as the self-driving stack which will run on our StreetDrone and PX2 to demonstrate Autonomous Valet Parking.

Autoware was started in 2015 by Professor Shinpei Kato at the Nagoya University, who presented it at ROSCon 2017. is based on ROS 1, which has certain fundamental design decisions that make it impractical for production autonomous cars. ROS 2, backed by Open Robotics, Intel, Amazon, Toyota and others, is quickly maturing, and from the very beginning was designed to fulfill the needs of not only researchers in academia, but also the emerging robotics industry.

Autoware.Auto launched in 2018 as an evolution of Autoware.AI, based on ROS 2, applying engineering best practices from the beginning, such as documentation, code coverage and testing, to build a production-ready open-source stack for autonomous driving with the guarantees in robustness and safety that the industry demands. We want to modularise and align with Autoware.Auto and move to ROS 2.

We want high quality software, we care about safety and we want to do things right. Parkopedia’s main contributions so far have been to improve the quality of the code by fleshing out the CI infrastructure, adding support for cross-compiling for ARM and the NVIDIA Drive PX2, modernising the message interfaces and developing a new driver to support 8 cameras, among other improvements.

Our plan for 2019 is to keep contributing to Autoware.AI and Autoware.Auto to support the StreetDrone ONE and to make whatever changes necessary to support our Autonomous Valet Parking demonstration.

We’re very grateful to Shinpei Kato and the Tier4 team for open-sourcing Autoware and for welcoming our contributions.

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