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include/dynamic_robot_localization

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README

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[![DOI](https://zenodo.org/badge/doi/10.5281/zenodo.34082.svg)](http://dx.doi.org/10.5281/zenodo.34082)

Dynamic Robot Localization
==================

## Overview

The dynamic_robot_localization is a ROS package that offers 3 DOF and 6 DOF localization using PCL and allows dynamic map update using OctoMap.
It's a modular localization pipeline, that can be configured using yaml files (detailed configuration layout available in [drl_configs.yaml](yaml/schema/drl_configs.yaml) and examples of configurations available in [guardian_config](https://github.com/inesc-tec-robotics/guardian_config/blob/hydro-devel/localization/guardian_localization.launch) and [dynamic_robot_localization_tests](https://github.com/carlosmccosta/dynamic_robot_localization_tests/tree/hydro-devel/launch/environments))

![Localization system processing pipeline](docs/overview.png "Localization system processing pipeline")

Figure 1: Localization system processing pipeline

[![3 DoF localization system tests with the Guardian platform in the CROB lab](http://img.youtube.com/vi/Rl6utteku8k/maxresdefault.jpg)](http://www.youtube.com/watch?v=Rl6utteku8k)

Video 1: 3 DoF localization and navigation tests with the Guardian platform in the CROB lab

[![3 DoF localization and navigation tests with the Guardian platform in ship interior](http://img.youtube.com/vi/NCh9ahU_Qcs/maxresdefault.jpg)](http://www.youtube.com/watch?v=NCh9ahU_Qcs)

Video 2: 3 DoF localization and navigation tests with the Guardian platform in ship interior

[![3 DoF localization, navigation and welding tests with the Guardian platform in ship interior](http://img.youtube.com/vi/CIP-QH87z8E/maxresdefault.jpg)](http://www.youtube.com/watch?v=CIP-QH87z8E)

Video 3: 3 DoF localization, navigation and welding tests with the Guardian platform in ship interior

[![3 DoF localization system tests with the Jarvis platform in the CROB lab](http://img.youtube.com/vi/DCYdJtbad18/maxresdefault.jpg)](http://www.youtube.com/watch?v=DCYdJtbad18)

Video 4: 3 DoF localization system tests with the Jarvis platform in the CROB lab

[![3 DoF localization system tests with the Pioneer platform in TUM RGB-D dataset](http://img.youtube.com/vi/jAJ5wiN-mJ8/maxresdefault.jpg)](http://www.youtube.com/watch?v=jAJ5wiN-mJ8)

Video 5: 3 DoF localization system tests with the Pioneer platform in the TUM RGB-D dataset

[![Free fly test with Kinect in the ETHZ RGB-D dataset using the 6 DoF localization system](http://img.youtube.com/vi/UslDiUkm7wE/maxresdefault.jpg)](http://www.youtube.com/watch?v=UslDiUkm7wE)

Video 6: Free fly test with Kinect in the ETHZ RGB-D dataset using the 6 DoF localization system

[![Rotations test with Kinect in the ETHZ RGB-D dataset using the 6 DoF localization system](http://img.youtube.com/vi/2OYWJIi9Rdk/maxresdefault.jpg)](http://www.youtube.com/watch?v=2OYWJIi9Rdk)

Video 7: Rotations test with Kinect in the ETHZ RGB-D dataset using the 6 DoF localization system

[![3 DoF localization system tests with the Guardian platform in Labiomep dataset](http://img.youtube.com/vi/VtOv4beLcBM/maxresdefault.jpg)](http://www.youtube.com/watch?v=VtOv4beLcBM)

Video 8: 3 DoF localization system tests with the Guardian platform in Labiomep dataset

[![Mapping with the Guardian robot in simulated ship interior using the 3 DoF localization system](http://img.youtube.com/vi/p4JaE6i7NqM/maxresdefault.jpg)](http://www.youtube.com/watch?v=p4JaE6i7NqM)

Video 9: Mapping with the Guardian robot in simulated ship interior using the 3 DoF localization system

[![Free fly mapping test with Kinect in the ETHZ RGB-D dataset using the 6 DoF localization system](http://img.youtube.com/vi/Us9XkcRx_5I/maxresdefault.jpg)](http://www.youtube.com/watch?v=Us9XkcRx_5I)

Video 10: Free fly mapping test with Kinect in the ETHZ RGB-D dataset using the 6 DoF localization system

## Data sources

The localization system can operate with any type of sensors that provide point clouds.
As such, it can use any number of laser sensors (by using the [laserscan_to_pointcloud](https://github.com/carlosmccosta/laserscan_to_pointcloud) package) or RGB-D / ToF cameras.

## Reference map

The reference map can be provided to the localization system from CAD ( .ply | .stl | .obj | .vtk ), pointcloud file (.pcd) / topic (sensor_msgs::PointCloud2) or using a 2D costmap (nav_msgs::OccupancyGrid).
To avoid unnecessary overhead it should be provided has pointcloud (.pcd).

To convert CAD files to .pcd there are two options:

1. Using [conversion scripts](tools) that filter the CAD using meshlabserver, add point curvature information and convert the CAD to .pcd (recommended)
 - Supported file types: .3ds .aln .apts .asc .dae .gts .obj .off .ply .pts .ptx .stl .tri .v3d .vrml .x3d .x3dv .xyz
2. Using the [mesh_to_pointcloud](https://github.com/carlosmccosta/mesh_to_pointcloud) package that converts CAD to .pcd directly (without curvature and filtering)
 - Supported file types: .3dc .3ds .asc .ac .bsp .dae .dw .dxf .fbx .flt .gem .geo .iv .ive .logo .lwo .lw .lws .md2 .obj .ogr .osg .pfb .ply .shp .stl .x .wrl

## Localization methods

The localization system has two different localization methods to tackle different scenarios.

For initial pose estimation and pose recovery it can use feature matching with several keypoint detectors (such as Scale Invariant Feature Transform (SIFT) and Intrinsic Shape Signatures (ISS3D)) and several keypoint descriptors (such as Point Feature Histogram (PFH), Fast Point Feature Histogram (FPFH), Signature of Histograms of Orientations (SHOT), Shape Context 3D (SC3D), Unique Shape Context (USC) and Ensemble of Shape Functions (ESF)).

For pose tracking it can use several variants of the Iterative Closest Point algorithm and also the Normal Distributions Transform (NDT).
Moreover, pose tracking can have two configurations. One tuned for the expected operation conditions of the robot and another able to recover from odometry estimation problems (the system can operate without odometry).

This gives the possibility to have a configuration that can perform pose tracking with the minimum amount of computational resources required (such as point-to-point ICP) and a more robust (and computational expensive) configuration to handle temporary tracking problems (such as point-to-point non linear ICP, point-to-plane ICP, generalized ICP).
Examples of the localization pipeline are available at [dynamic_robot_localization/yaml/configs](yaml/configs)

## Localization reliability

The localization pipeline allows the attachment of transformation validators before publishing pose estimations.

They can be used to suppress estimations that are not plausible for a given robot operation conditions.

These validators can suppress localization estimations in which the pose correction has a [ outlier percentage | inliers root mean square error | inliers / outliers angular distribution | translation / rotation corrections ] outside acceptable thresholds.

If the tracking methods can't recover after a given amount of sensor updates / time, the initial pose estimation (using features) can be activated.

## Dynamic map update

Dynamic map update can be performed with / without surface reconstruction and can also be coupled with the [OctoMap](http://octomap.github.io/) package in order to perform probabilistic point cloud integration (useful for dynamic environments in which large objects may appear / disappear),

This can be by setting the reference_pointclouds/reference_pointcloud_update_mode parameter to one of the available integration modes (NoIntegration | FullIntegration | InliersIntegration | OutliersIntegration) and configuring [OctoMap](launch/octo_map.launch) to use the registered cloud or the inlier / outlier cloud as input (and the output of OctoMap as the reference cloud for the localization system).

## Usage

The localization system can be started with [dynamic_robot_localization_system.launch](launch/dynamic_robot_localization_system.launch)

This launch file starts the nodes:

* dynamic_robot_localization
* pose_to_tf_publisher (can be disabled)
* laserscan_to_pointcloud_assembler (can be disabled)
* octomap (can be disabled)

The localization is integrated with other ROS nodes by publishing TF transforms between map and odom frames (configurable frame names) using the [pose_to_tf_publisher](https://github.com/carlosmccosta/pose_to_tf_publisher) node.

The initial pose can be given in yaml / roslaunch files or dynamically using [geometry_msgs::PoseStamped](http://docs.ros.org/api/geometry_msgs/html/msg/PoseStamped.html) / [geometry_msgs::PoseWithCovarianceStamped](http://docs.ros.org/api/geometry_msgs/html/msg/PoseWithCovarianceStamped.html) topics / rviz.

The dynamic_robot_localization node publishes [geometry_msgs::PoseStamped](http://docs.ros.org/api/geometry_msgs/html/msg/PoseStamped.html) / [geometry_msgs::PoseWithCovarianceStamped](http://docs.ros.org/api/geometry_msgs/html/msg/PoseWithCovarianceStamped.html) that are received by pose_to_tf_publisher and converted to TF messages.

The TF is published in a separate ROS node to ensure that the system remains operational and with constant TF publish rate even if the localization node has variable computational time.

Moreover, in the remote case that the localization system crashes (by lack of system resources, programming error in the localization node or in one of its external libraries), the system can remain operational using only odometry for a given amount of time (i.e. TF messages can be published continuously, with a timeout counted after the last valid pose estimation or one message for each pose estimation) and the localization node can be automatically restarted (has node respawn activated) without any impact in the rest of the system.

The dynamic_robot_localization node publishes in latched topics (if required, as the publish of these messages can be disabled to reduce consumption of system resources):

1. dynamic_robot_localization::LocalizationDetailed
 - Message that has detailed information about the localization estimation (number of registered points | number of inliers | outlier percentage | inliers root mean square error | inliers / outliers angular distribution | translation / rotation corrections | global estimation accepted poses)
2. dynamic_robot_localization::LocalizationTimes
 - Message with the computation time of each localization processing stage (and also the global time)
3. dynamic_robot_localization::LocalizationDiagnostics
 - Message with diagnostics (currently only has the point clouds size after each major localization pipeline stages)
4. Registered clouds (sensor_msgs::PointCloud2)
 - The full registered cloud or its points categorized as inliers / outliers (computed in each pose estimation) is also published as 3 separated point clouds.
5. Reference point cloud (sensor_msgs::PointCloud2)
 - The current reference point cloud is also published in order to be available to localization supervisors

## Dynamic robot localization tests

The main tests performed using the dynamic_robot_localization package are available in [dynamic_robot_localization_tests](https://github.com/carlosmccosta/dynamic_robot_localization_tests).

## Installation and package dependencies

Installation scripts can be found in the [install folder.](install/)

## List of related git repositories:

* [dynamic_robot_localization_tests](https://github.com/carlosmccosta/dynamic_robot_localization_tests)
* [pose_to_tf_publisher](https://github.com/carlosmccosta/pose_to_tf_publisher)
* [laserscan_to_pointcloud](https://github.com/carlosmccosta/laserscan_to_pointcloud)
* [mesh_to_pointcloud](https://github.com/carlosmccosta/mesh_to_pointcloud)
* [robot_localization_tools](https://github.com/carlosmccosta/robot_localization_tools)
* [crob_gazebo_models](https://github.com/carlosmccosta/crob_gazebo_models)
* [octomap_mapping](https://github.com/carlosmccosta/octomap_mapping)

## More info

* [Dissertation webpage](http://carlosmccosta.wix.com/personal-webpage#!dissertation/c12dl)
* [Dissertation pdf](https://www.researchgate.net/publication/277752192_Robot_Self-Localization_in_Dynamic_Environments)
* [Dissertation latex](https://github.com/carlosmccosta/dynamic_robot_localization_dissertation)
* [Dissertation abstracts](https://github.com/carlosmccosta/dynamic_robot_localization_dissertation_abstracts)
* [ICIT-2015 conference paper](https://www.researchgate.net/publication/275958401_Robust_and_Accurate_Localization_System_for_Mobile_Manipulators_in_Cluttered_Environments)
* [InTech Cutting Edge Research in Technologies book chapter](https://www.researchgate.net/publication/283119505_3_DoF6_DoF_Localization_System_for_Low_Computing_Power_Mobile_Robot_Platforms)
* [Robotics and Autonomous Systems journal article](https://www.researchgate.net/publication/283515930_Robust_36_DoF_self-localization_system_with_selective_map_update_for_mobile_robot_platforms)
* [Results folder](https://www.dropbox.com/sh/nwb6gezj2dan187/AABM2u4BGd12lN__nYFwSktLa?dl=0)

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