amcl ROS

AMCL是机器人在二维地图移动过程中的概率定位系统。它应用自适应的蒙特卡洛定位方式（或者KLD采样），采用微粒过滤器来跟踪已知地图中机器人的位姿。

1.算法

在Probabilistic Robotics一书中，很好的对AMCL算法和参数进行描述。建议用户查看更多的细节。特别是，我们使用以下的算法： sample_motion_model_odometry, beam_range_finder_model, likelihood_field_range_finder_model, Augmented_MCL, and KLD_Sampling_MCL。

目前完成的，这个节点只用于laser scans和laser map。它可以被扩展应用与其他的激光数据工作。

2.举例

在base_scan的topic上利用激光数据来定位：

amcl scan:=base_scan
           

3.节点

AMCL需要输入laser-based map, laser scans, 和TF转换，并且输出位置估计。在启动时，AMCL根据所提供的参数来初始化它的颗粒过滤器。由于默认原因，如果没有参数设置，初始过滤状态以一个中等大小的粒子云为中心（0,0,0）。

有三种类型的ROS参数可以用来配置AMCL节点：整体滤波器，激光模式，odometery模型。

Subscribed Topics

scan  ( sensor_msgs/LaserScan )

• Laser scans.

tf  ( tf/tfMessage )

• Transforms.

initialpose  ( geometry_msgs/PoseWithCovarianceStamped )

• Mean and covariance with which to (re-)initialize the particle filter.

map  ( nav_msgs/OccupancyGrid )

• When the use_map_topic parameter is set, AMCL subscribes to this topic to retrieve the map used for laser-based localization. New in navigation 1.4.2.

Published Topics

amcl_pose  ( geometry_msgs/PoseWithCovarianceStamped )

• Robot's estimated pose in the map, with covariance.

particlecloud  ( geometry_msgs/PoseArray )

• The set of pose estimates being maintained by the filter.

tf  ( tf/tfMessage )

• Publishes the transform from odom (which can be remapped via the ~odom_frame_id parameter) to map.

Services

global_localization  ( std_srvs/Empty )

• Initiate global localization, wherein all particles are dispersed randomly through the free space in the map.

Services Called

static_map  ( nav_msgs/GetMap )

• amcl calls this service to retrieve the map that is used for laser-based localization; startup blocks on getting the map from this service.

Parameters

There are three categories of ROS Parameters that can be used to configure the amcl node: overall filter, laser model, and odometery model.

Overall filter parameters

~min_particles (int, default: 100)

• Minimum allowed number of particles.

~max_particles  ( int , default: 5000)

• Maximum allowed number of particles.

~kld_err  ( double , default: 0.01)

• Maximum error between the true distribution and the estimated distribution.

~kld_z  ( double , default: 0.99)

• Upper standard normal quantile for (1 - p), where p is the probability that the error on the estimated distrubition will be less than kld_err.

~update_min_d  ( double , default: 0.2 meters)

• Translational movement required before performing a filter update.

~update_min_a  ( double , default: π/6.0 radians)

• Rotational movement required before performing a filter update.

~resample_interval  ( int , default: 2)

• Number of filter updates required before resampling.

~transform_tolerance  ( double , default: 0.1 seconds)

• Time with which to post-date the transform that is published, to indicate that this transform is valid into the future.

~recovery_alpha_slow  ( double , default: 0.0 ( disabled ))

• Exponential decay rate for the slow average weight filter, used in deciding when to recover by adding random poses. A good value might be 0.001.

~recovery_alpha_fast  ( double , default: 0.0 ( disabled ))

• Exponential decay rate for the fast average weight filter, used in deciding when to recover by adding random poses. A good value might be 0.1.

~initial_pose_x  ( double , default: 0.0 meters)

• Initial pose mean (x), used to initialize filter with Gaussian distribution.

~initial_pose_y  ( double , default: 0.0 meters)

• Initial pose mean (y), used to initialize filter with Gaussian distribution.

~initial_pose_a  ( double , default: 0.0 radians)

• Initial pose mean (yaw), used to initialize filter with Gaussian distribution.

~initial_cov_xx  ( double , default: 0.5*0.5 meters)

• Initial pose covariance (x*x), used to initialize filter with Gaussian distribution.

~initial_cov_yy  ( double , default: 0.5*0.5 meters)

• Initial pose covariance (y*y), used to initialize filter with Gaussian distribution.

~initial_cov_aa  ( double , default: (π/12)*(π/12) radian)

• Initial pose covariance (yaw*yaw), used to initialize filter with Gaussian distribution.

~gui_publish_rate  ( double , default: -1.0 Hz)

• Maximum rate (Hz) at which scans and paths are published for visualization, -1.0 to disable.

~save_pose_rate  ( double , default: 0.5 Hz)

• Maximum rate (Hz) at which to store the last estimated pose and covariance to the parameter server, in the variables ~initial_pose_* and ~initial_cov_*. This saved pose will be used on subsequent runs to initialize the filter. -1.0 to disable.

~use_map_topic  ( bool , default: false)

• When set to true, AMCL will subscribe to the map topic rather than making a service call to receive its map. New in navigation 1.4.2

~first_map_only  ( bool , default: false)

• When set to true, AMCL will only use the first map it subscribes to, rather than updating each time a new one is received. New in navigation 1.4.2

Laser model parameters

Note that whichever mixture weights are in use should sum to 1. The beam model uses all 4: z_hit, z_short, z_max, and z_rand. The likelihood_field model uses only 2: z_hit and z_rand.

~laser_min_range (double, default: -1.0)

• Minimum scan range to be considered; -1.0 will cause the laser's reported minimum range to be used.

~laser_max_range  ( double , default: -1.0)

• Maximum scan range to be considered; -1.0 will cause the laser's reported maximum range to be used.

~laser_max_beams  ( int , default: 30)

• How many evenly-spaced beams in each scan to be used when updating the filter.

~laser_z_hit  ( double , default: 0.95)

• Mixture weight for the z_hit part of the model.

~laser_z_short  ( double , default: 0.1)

• Mixture weight for the z_short part of the model.

~laser_z_max  ( double , default: 0.05)

• Mixture weight for the z_max part of the model.

~laser_z_rand  ( double , default: 0.05)

• Mixture weight for the z_rand part of the model.

~laser_sigma_hit  ( double , default: 0.2 meters)

• Standard deviation for Gaussian model used in z_hit part of the model.

~laser_lambda_short  ( double , default: 0.1)

• Exponential decay parameter for z_short part of model.

~laser_likelihood_max_dist  ( double , default: 2.0 meters)

• Maximum distance to do obstacle inflation on map, for use in likelihood_field model.

~laser_model_type  ( string , default:  "likelihood_field" )

• Which model to use, either beam, likelihood_field, or likelihood_field_prob (same as likelihood_field but incorporates the beamskip feature, if enabled).

Odometery model parameters

If ~odom_model_type is "diff" then we use the sample_motion_model_odometry algorithm from Probabilistic Robotics, p136; this model uses the noise parameters odom_alpha_1 through odom_alpha4, as defined in the book.

If ~odom_model_type is "omni" then we use a custom model for an omni-directional base, which uses odom_alpha_1 throughodom_alpha_5. The meaning of the first four parameters is similar to that for the "diff" model. The fifth parameter capture the tendency of the robot to translate (without rotating) perpendicular to the observed direction of travel.

~odom_model_type (string, default: "diff")

• Which model to use, either "diff" or "omni".

~odom_alpha1  ( double , default: 0.2)

• Specifies the expected noise in odometry's rotation estimate from the rotational component of the robot's motion.

~odom_alpha2  ( double , default: 0.2)

• Specifies the expected noise in odometry's rotation estimate from translational component of the robot's motion.

~odom_alpha3  ( double , default: 0.2)

• Specifies the expected noise in odometry's translation estimate from the translational component of the robot's motion.

~odom_alpha4  ( double , default: 0.2)

• Specifies the expected noise in odometry's translation estimate from the rotational component of the robot's motion.

~odom_alpha5  ( double , default: 0.2)

• Translation-related noise parameter (only used if model is "omni").

~odom_frame_id  ( string , default:  "odom" )

• Which frame to use for odometry.

~base_frame_id  ( string , default:  "base_link" )

• Which frame to use for the robot base

~global_frame_id  ( string , default:  "map" )

• The name of the coordinate frame published by the localization system

~tf_broadcast  ( bool , default: true)

• Set this to false to prevent amcl from publishing the transform between the global frame and the odometry frame.

3.1.6Transforms

AMCL将传入的激光扫描数据转为里程计结构（odom_frame_id）。因此，必须存在从激光发布到里程计的tf树转换。

实现细节：在接收第一个激光扫描，AMCL查找激光结构和base计结构的TF转换，并且锁存。所以AMCL不能处理激光于base相对移动的情况。

下面的图片显示用里程计和AMCL定位的不同之处。在操作过程中，AMCL估计base结构相当于global结构TF转换，但是，它只是放global和里程计之间的TF转换。从本质上，这种转换利用航位推算来处理漂移，所发布的转换是远期的。

Odometry Location -- 只是通过里程计的数据来处理/base和/odom之间的TF转换；

AMCL  Map Location -- 查找/base和激光的TF。/base通过/odom在/map中行走，机器人根据已知激光数据，估计/base相对于相对于/global的TF，那么我们可以知道/map和/base之间的TF，从而估计位置。