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i_see_pee

i_see_pee is a 2d localization package. At its core it uses an icp algorithm to determine the current robot position. Its main advantage is that it performs one optimization step linear in time: O(N).

Whats the difference

There are many stellar packages for localization which rely on icp.

• libpointmatcher
• cartographer
• dynamic_robot_localization

These are generic localization/slam packages. They support 2d and 3d environment, offer rich features for sensor processing and what not. Which is nice. i_see_pee is different. It just offers 2d localization. But its smaller. And its way faster.

The speed

A typical icp algorithm consists of two steps:

• k nearest neighbors matching
• solving the least squares problem.

The least squares problem is typically O(N), which is nice. The k nearest neighbors is a little more expensive. Typically the lookup is implemented using a knn-tree. The lookup there comes at O(D log M), where D is the dimension and M the size of the occupied points on the map.

When aiming for a generic setup, there is little one can do. i_see_pee leverages the fact that all grid_maps are discrete and that the registration cloud (map) is known in advance. It pre-computes for cells close to walls the k nearest neighbors and stores them in a hash-table. When a sensor reading falls in one of these cells, all its k nearest neighbors can be retrieved in linear time.

Compiling

You will require Eigen for the compilation. Besides that the package can be compiled as a standard ros-package.

If you use catkin tools you can compile the package with:

cd catkin_ws/src
git clone https://github.com/dorezyuk/i_see_pee.git
catkin build i_see_pee -DCMAKE_BUILD_TYPE=Release

Hint: make sure to compile the library as release

Depending on your processor you should enable vectorization for Eigen.

Configuring

Subscriptions

map/topic

• topic under which the static map is published
• type: string
• default: map
• message_type: nav_msgs::OccupancyGrid

scan/topic

• topic under which the sensor data is published
• type: string
• default: scan
• message_type: sensor_msgs::LaserScan

Frames

odom/map_frame

• frame_id which defines the global frame.
• type: string
• default: map

odom/base_frame

• frame_id which defines the base frame. You can use either only odometry, which is provided by the differential_drive controller or fuse imu and odometry to improve the initial guess.
• type: string
• default: base_link

odom/base_frame

• frame_id of the odom frame of the robot.
• type: string
• default: odom

Mapping parameters

map/k

• number of neighbors to consider
• type: int
• default: 5 (min: 1, max: 10)

map/radius

• number of cells to consider from an occupied cell. This massively determines the size of the hash map
• type: int
• default: 5 (min: 0, max: 10)

Optimization parameters

icp/t_norm

• translation norm in meter defining convergence of the icp: if the generated transform is smaller than icp/t_norm and icp/r_norm the icp is said to be converged.
• type: float
• default: 0.01 (min: 0.01)

icp/r_norm

• rotation norm defining convergence (see [icp/t_norm])
• type: float
• default: 0.01 (min: 0.01)

icp/max_iter

• defines the maximum number of iterations of the icp optimization. The looping stops, if the norm of the generated correction transform is smaller then icp/t_norm and icp/r_norm
• type: int
• default: 100 (min: 1, max: 100)

icp/stride

• in order to speed up the computation one can reduce the point cloud size. Applying a stride of 'n' will take only every nth point into consideration. The starting point defines hence which points will be drawn. It is drawn at random.
• type: int
• default: 3 (min: 1, max: 100)

Understanding

When you want to play with the code, you should be lucky: i_see_pee is very small (less then 1000 loc currently). As you might find, everything related to specific ros-types (msgs, tf) is shielded away behind an interface.

The main files are undoubtedly i_see_pee.hpp/cpp. They contain several namespaces:

• map: everything related to the static map. This includes a ros-interface and the algorithm to extract knns from a grid and to store them in a hash-map
• scan: everything related to the sensor data. This includes the ros-interface to sensor_msgs::LaserScan and some caching to convert it to some internal representation.
• odom: everything related to frames/odometry.
• icp: everything related to the math part (which was heavily borrowed from libpointmatcher)

Let me know how your experience was with the package.