A point cloud is a set of points in 3-D space. Point clouds are typically obtained from 3-D scanners, such as a lidar or Kinect® device. They have applications in robot navigation and perception, depth estimation, stereo vision, visual registration, and advanced driver assistance systems (ADAS).
Point cloud registration is the process of aligning two or more 3-D point clouds of the same scene into a common coordinate system. Mapping is the process of building a map of the environment around a robot or a sensor. Registration and mapping can be used to reconstruct a 3-D scene or build a map of a roadway for localization. While registration is commonly followed by mapping, there are other applications using registration, such as deformable motion tracking, which may not require mapping. Computer Vision Toolbox™ algorithms provide functions for performing point cloud registration and mapping. The workflow consists of preprocessing, registration, drift correction, and alignment of point clouds.
Follow these steps to perform point cloud registration and mapping on a sequence of point clouds.
Preprocess Point Clouds — To prepare the point clouds for registration, downsample them and remove unwanted features and noise.
Register Point Clouds — Register each point cloud against the one preceding it. These registrations are used in Odometry, which is the process of accumulating a registration estimate over successive frames. Using odometry alone can lead to drift between the measured and ground truth poses.
Detect Loops — To minimize drift, you must identify the return of the sensor to a previously visited location, forming a loop in the trajectory of the sensor. This is referred to as loop closure detection.
Correct Drift — Use the detected loops to minimize drift through pose graph optimization, which consists of incrementally building a pose graph by adding nodes and edges, and then optimizing the pose graph once sufficient loops are found. The result of pose graph optimization is a set of optimized absolute poses.
Assemble Map — Assemble a map by aligning the registered point clouds using their optimized absolute poses.
Use these objects to manage data associated with the point cloud registration and mapping workflow:
pointCloud object — The point
cloud object stores a set of points located in 3-D space. It uses efficient
indexing strategies to accomplish nearest neighbor searches, which are
leveraged by point cloud preprocessing and registration functions.
rigid3d object — The rigid 3-D object stores a 3-D rigid
geometric transformation. In this workflow, it represents the relative and
pcviewset object — The point cloud view set object manages the
data associated with the odometry and mapping process. It organizes data as
a set of views and pairwise connections between views. It also builds and
updates a pose graph.
Each view consists of a point cloud and the associated absolute pose transformation. Each view has a unique identifier within the view set and forms a node of the pose graph.
Each connection stores information that links one view to another view. This includes the relative transformation between the connected views and the uncertainty involved in computing the measurement. Each connection forms an edge in the pose graph.
Preprocessing includes removing unwanted features and noise from the point clouds, as well segmenting or downsampling them. Preprocessing can include these functions:
pcdenoise — Remove unwanted
noise from the point cloud.
You can use the
pcregistercorr function to register a moving point cloud to a fixed
point cloud. The registration algorithms used by these functions are based on the
iterative closest point (ICP) algorithm, the coherent point drift (CPD) algorithm, the
normal-distributions transform (NDT) algorithm, and a phase correlation algorithm,
respectively. For more information on these algorithms, see References.
When registering a point clouds, choose the type of transformation that represents how objects in the scene change between them.
|Rigid||The rigid transformation preserves the shape and size of objects in the scene. Objects in the scene can undergo translations, rotations, or both. The same transformation applies to all points.|
|Affine||The affine transformation allows the objects to shear and change scale in addition to translations and rotations.|
|Nonrigid||The nonrigid transformation allows the shape of objects in the scene to change. Points undergo distinct transformations. A displacement field represents the transformation.|
This table compares the point cloud registration function options, their transformation types, and their performance characteristics. Use this table to help you select the appropriate registration function for your use case.
|Registration Method (function)||Transformation Type||Description||Performance Characteristics|
||Fast registration method, but generally slower than ICP|
Local registration method that relies on an initial transform estimate
|Fastest registration method|
|Rigid, affine, and nonrigid|
Global method that does not rely on an initial transformation estimate
|Slowest registration method|
|Rigid||Registration method that relies on an occupancy grid, assigning probability values to the grid based on the Z-coordinate values of points within each grid cell.|
Best suited for ground vehicle navigation
Increasing the size of the occupancy grids increases the computational requirements of the function.
Registering the current (moving) point cloud against the previous
(fixed) point cloud returns a
transformation that represents the estimated relative pose of the moving point cloud in
the frame of the fixed point cloud. Composing this relative pose transformation with all
previously accumulated relative pose transformations gives an estimate of the absolute
Add the odometry edge, an edge defined by the connection
between successive views, formed by the relative pose transformation between the fixed
and moving point clouds to the
object using the
Using odometry alone leads to drift due to an accumulation of errors. These errors can result in severe inaccuracies over long distances. Using graph-based simultaneous localization and mapping (SLAM) corrects the drift. To do this, detect loop closures by finding a location visited in a previous point cloud using descriptor matching. Close the loop to correct the drift. Follow these steps for loop detection and closure:
scanContextDescriptor function to extract scan context
descriptors, which capture the distinctiveness of a view, from two point
clouds in the view set.
scanContextDistance function to compute the descriptor
distance between the two scan context descriptors. If the distance between
two descriptors is below a specified threshold, then it is a potential loop
Register the point clouds to determine the relative pose transformation
between the views and the root mean square error (RMSE) of the Euclidean
distance between the aligned point clouds. Use the RMSE to filter invalid
loop closures. The relative pose transformation represents a connection
between the two views. An edge formed by a connection between nonsuccessive
views is called a loop closure edge. You can add the
connection to the
pcviewset object using the
object internally updates the pose graph as views and connections are added. To minimize
drift, perform pose graph optimization by using the
optimizePoses function, once sufficient loop closure. The
optimizePoses function returns a
object with the optimized absolute pose transformations for each view.
You can use the
createPoseGraph function to return the pose graph as a MATLAB®
digraph object. You can use graph algorithms in MATLAB to inspect, view, or modify the pose graph. Use the
optimizePoseGraph (Navigation Toolbox) function from the Navigation Toolbox™ to optimize the modified pose graph, and then use the
updateView function to update the poses in the view set.
function to build a point cloud map using the point clouds from the view set and their
optimized absolute pose transformations.
Local registration methods, such as those that use NDT or ICP (
respectively), require initial estimates. To obtain an initial estimate, use
another sensor such as an inertial measurement unit (IMU) or other forms of
odometry. Improving the initial estimate helps the registration algorithm
Specifying the higher values for the
argument or lower values for the
'Tolerance' property for
more accurate registration results, but slower registration speeds.
 Myronenko, Andriy and Xubo Song. “Point Set Registration: Coherent Point Drift.” IEEE Transactions on Pattern Analysis and Machine Intelligence 32, no. 12 (December 2010): 2262–75.
 Chen, Yang, and Gérard Medioni. “Object Modelling by Registration of Multiple Range Images.” Image and Vision Computing 10, no. 3 (April 1992): 145–55.
 Besl, P.J., and Neil D. McKay. “A Method for Registration of 3-D Shapes.” IEEE Transactions on Pattern Analysis and Machine Intelligence 14, no. 2 (February 1992): 239–56.
 Biber, P., and W. Strasser. “The Normal Distributions Transform: A New Approach to Laser Scan Matching.” In Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453), 3:2743–48. Las Vegas, Nevada, USA: IEEE, 2003.
 Magnusson, Martin. “The Three-Dimensional Normal-Distributions Transform: An Efficient Representation for Registration, Surface Analysis, and Loop Detection.” Örebro University, 2009.
 Dimitrievski, Martin, David Van Hamme, Peter Veelaert, and Wilfried Philips. “Robust Matching of Occupancy Maps for Odometry in Autonomous Vehicles:” In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 626–33. Rome, Italy: SCITEPRESS - Science and and Technology Publications, 2016.