Amcl Package ##HOT## Download

At the conceptual level, the AMCL package maintains a probability distribution over the set of all possible robot poses, and updates this distribution using data from odometry and laser range-finders. Depth cameras can also be used to generate these 2D laser scans by using the package depthimage_to_laserscan which takes in depth stream and publishes laser scan on sensor_msgs/LaserScan. More details can be found on the ROS Wiki.

Even though the AMCL package works fine out of the box, there are various parameters which one can tune based on their knowledge of the platform and sensors being used. Configuring these parameters can increase the performance and accuracy of the AMCL package and decrease the recovery rotations that the robot carries out while carrying out navigation.

Amcl Package Download

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There are three categories of ROS Parameters that can be used to configure the AMCL node: overall filter, laser model, and odometery model. The full list of these configuration parameters, along with further details about the package can be found on the webpage for AMCL. They can be edited in the amcl.launch file.

In this lab, we will use amcl package for localization. The amcl algorithmimplements Monte Carlo localization for state estimation. In the first part ofthe lab, you will use amcl with a map that has already been built for you. Inthe second part, you will build your own map using the gmapping package andthen use the resulting map for localization.

This launch file will start the amcl node with certain predefined parameters. Italso loads an existing map and publishes it in the /map topic. You can openthe file ~/catkin_ws/src/cmuq-turtlebot-remote/amcl_demo/amcl_demo.launch tosee the configuration.

In this lesson in the series of lessons on ROS tutorials, we will first understand what navigation, path planning, and control mean in robotics. Then, we will launch the navigation package (amcl package) in Gazebo to localize a robot during motion. Afterward, we will set an autonomous motion for a robot starting from the initial pose to the target pose using RVIZ.

For my robot localisation Im using only individuals laser scans and amcl. But my amcl jumps and my results are not so good. For example when Im calculating some parameters like velocity or distance to some object the results are not so good. Im using IMU data only in for the ICP in the laser scan matcher package. But for other purposes not.

So my problem is how to improve the results?? For example for my linear velocity parameters im using exclusive the amcl pose. But how to improve that using IMU data or for example extended kalman filter. I dont have any odometry. My odometry is all based on the laser. In that way is my pose estimated, only using laser and amcl.

So how to improve the robot heading, motion estimation based on the sensor package I have. So with laser scans, IMU and camera (for the kinect), how to get a better results in sense of motion estimation, heading, and obtain some parameters like linear and angular velocity. Any help??

Comment by dornhege on 2012-11-27:

Here comes the hugely overkill approach if you really need localization instead of SLAM: Use hector_mapping to produce the odometry transform and feed that into amcl.

Comment by Astronaut on 2012-11-27:

so i can use hactor_mapping package , than publish odometry on that topic. And than feeding it into robot_pose_ekf. So I will use hector mapping and robot_pose_ekf instead of using IMU and AMCL? OR I understand wrong??

Comment by Astronaut on 2012-12-03:

But as in ROS wiki documentation said robot_pose_ekf implements an extended Kalman filter for determining the robot pose. That is what Im looking for. An accurate pose estimation as my amcl "jumps"

Comment by Astronaut on 2012-12-03:

So I do not see any reason to use robot_pose_ekf . Because my sensor package consit of laser, IMU and camera. I do not know if robot_pose_ekf can givemore consistent estimates fusing IMU and the camera ( visio2 package).

Comment by gleb on 2016-10-04:

Hi, I want to use amcl with odom from hector_mapping combined with imu_data, but I am not able to get it running. I get tf errors, since I am missing tf from map to odom and from odom to base_footprint. How can I feed hectors odom to robot_pose_ekf and make robot_pose_ekf work together with amcl?

Comment by dornhege on 2012-11-27:

AFAIK robot_pose_ekf doesn't do any scan filtering. It just "merges" probability distributions. You best choice would be to use laser_scan_matcher to provide an odometry transform and pass that into amcl. Alternatively if full SLAM would be OK, look at hector_slam.

The move_base package provides an implementation of an action that, given a goal in the world, willattempt to reach it with a mobile base. The move_base node links together a global and localplanner to accomplish its global navigation task. The move_base node also maintains two costmaps,one for the global planner, and one for a local planner (see the costmap_2d package) that are usedto accomplish navigation tasks.

The parameter configuration files of the amcl package are: amcl_param_diff.yaml (the file is theamcl parameter file used in the four-wheel differential, omnidirectional wheel, and track motionmodes), and amcl_param.yaml (the file is the amcl parameter file used in the Ackermann motion mode).

Currently, the AMCL module in ROS 2 Navigation System is a direct port from ROS1 AMCL package with some minor code re-factoring. The direct port includes all of ROS1 functionalities except running from Bag files. The code re-factoring includes moving Sensor, Particle Filter, and Map from nav2_amcl directory to nav2_util directory. The main logic behind this is code re-usability. However, given the way in which these classes are currently written, these codes are highly bounded to AMCL. Therefore, to make use of code re-usability, these classes need to be substantially modified.

I would like to print /amcl_pose same as the ground_truth/state default time-step. Hence, I have developed coding to subscribe /amcl_pose and print it in looping using while not rospy.is_shutdown() as shown in the figure below. I have developed this coding because when I used rostopic echo /amcl_pose its only print one value of the pose at one time, not based on default time-step. But, my coding have problem. When I move the robot to another place, the x,y,z value is not updated and still print the initial value of the robot's pose. How can I print the /amcl_pose value same as the ground_truth/state default time-step?

After building a map of the environment, the next thing we need to implement is localization. The robot should localize itself on the generated map. In this section, we will see a detailed study of the amcl package and the amcl launch files usedin Chefbot.

Hi Lukasz,

Yes when I ran the launch separately it worked. I was able to see the image on rviz and even the April tag detection and TF of the camera and tag. The issue occurs when I run it along with amcl. We use

Logitec c920 pro HD webcam.

The ROS Navigation Stack requires the use of AMCL (Adaptive Monte Carlo Localization), a probabilistic localization system for a robot. AMCL is used to track the pose of a robot against a known map. It takes as input a map, LIDAR scans, and transform messages, and outputs an estimated pose. You can learn more about this package here on the ROS website.

AMCL and gmapping (or some other SLAM algorithm) are important parts for mapping process. AMCL (Adaptive Monte Carlo Localization) is a probalistic localization system for a robot moving in 2D (Source: ROS amcl). In ROS, amcl is a node which uses a map, laser scans, and transform messages, and outputs pose estimates.

The gmapping package contains the ROS node slam_gmapping, which builds a 2D map, using data on laser sensor measurements and the data on the position of the mobile robot. The node slam_gmapping (in our simulation this node is named gmapping) is subscribed to the topics /scan and /tf, in order to obtain the necessary data to build the map. This node reads data from the laser and transformations and then creates OGM (Occupancy Grid Map) based on that data. The map that is being created is being published on the topic /map throughout the mapping process. Topic /map uses a message of type nav_msgs/OccupancyGrid. In OGM, the occupancy is represented by the integer value in the range from 0 to 100. A value of 0 means completely unoccupied and 100 means fully occupied (e.g., wall), and a special value is -1, which means an unknown area. (Source: Mapping & Create a map from zero)

I also check the topics, and I can see that the map topic exists, but when I echo it, nothing is published to it. I find it weird that the output from amcl package is saying that it receives the map I made in part 2 of this tutorial.

In this tutorial, we will walk you through the map creation process and discuss the SLAM algorithm, which consists of two parts: mapping and localization. The gmapping package will be used for mapping, the amcl package will be used to locate the robot on the map. Mapping is usually a necessary element to create more advanced autonomous driving systems. In addition, thanks to the knowledge of the map, we can complete the location of the robot. This is because we can compare the currently received sensor data and match it to the map. To do this, you will need to collect data from the RPLIDAR on board your ROSbot. e24fc04721