robot_localization实现imu和odom融合

记录使用robot_localization进行融合下载地址git clone https://gitee.com/bingshuibuliang/robot_localization.git注意/odometry/filtered是这个节点发送的融合位姿修改的话需要在ekf_nodelet_template.launch里在使用robot_localization实现imu和odom融合前需要发布tf关系remap fromodometry/filtered toodom/使用ekf_nodelet_template.launch修改ekf_template.yaml参数在params文件夹frequency: 50 silent_tf_failure: false sensor_timeout: 0.1 two_d_mode: true # 地面机器人必须开 transform_time_offset: 0.0 transform_timeout: 0.0 print_diagnostics: true debug: false publish_tf: true publish_acceleration: false permit_corrected_publication: false map_frame: map odom_frame: odom base_link_frame: base_link world_frame: odom # # 里程计只融合 x y 位置 线速度 vx # odom0: /ranger_base_node/odom odom0_config: [true, true, false, false, false, false, true, false, false, false, false, false, false, false, false] odom0_queue_size: 5 odom0_differential: false odom0_relative: false # # IMU只融合 姿态 角速度完美修正里程计角度 # imu0: /imu/data imu0_config: [false, false, false, true, true, true, # 融合 roll pitch yaw角度由IMU提供 false, false, false, false, false, true, # 只融合yaw角速度 false, false, false] # 不融合加速度避免震动干扰 imu0_nodelay: false imu0_differential: false imu0_relative: true imu0_queue_size: 10 imu0_remove_gravitational_acceleration: true # # 关闭控制输入避免干扰融合 # use_control: false # [ADVANCED] The process noise covariance matrix can be difficult to tune, and can vary for each application, so it is # exposed as a configuration parameter. This matrix represents the noise we add to the total error after each # prediction step. The better the omnidirectional motion model matches your system, the smaller these values can be. # However, if users find that a given variable is slow to converge, one approach is to increase the # process_noise_covariance diagonal value for the variable in question, which will cause the filters predicted error # to be larger, which will cause the filter to trust the incoming measurement more during correction. The values are # ordered as x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Defaults to the matrix below if # unspecified. process_noise_covariance: [0.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.05, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.06, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.03, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.03, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.06, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.025, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.025, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.02, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.015] # [ADVANCED] This represents the initial value for the state estimate error covariance matrix. Setting a diagonal # value (variance) to a large value will result in rapid convergence for initial measurements of the variable in # question. Users should take care not to use large values for variables that will not be measured directly. The values # are ordered as x, y, z, roll, pitch, yaw, vx, vy, vz, vroll, vpitch, vyaw, ax, ay, az. Defaults to the matrix below #if unspecified. initial_estimate_covariance: [1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1e-9]*publish_tf**如果为 true状态估计节点会将 world_frame 参数指定的帧的变换发布到其子节点。如果world_frame与map_frame相同它将发布从map_frame到odom_frame的变换如果world_frame与odom_frame相同它将发布从odom_frame到base_link_frame的变换。默认为 true。 **Coordinate frame specification**: - “map_frame”、“odom_frame”和“base_link_frame”的默认值分别是“map”、“odom”和“base_link”。“world_frame”参数默认为“odom_frame”的值。 - 如果您的系统没有map_frame只需删除或注释它并确保“world_frame”设置为“odom_frame”的值。 - 如果您仅融合连续位置数据例如车轮编码器里程计、视觉里程计或 IMU 数据请将“world_frame”设置为“odom_frame”的值。这是 robots_localization 中状态估计节点的默认行为也是它最常见的用途。这不是我们的情况因为我们还使用不连续的 GPS 数据。 - 如果您正在融合受离散跳跃影响的全球绝对位置数据例如GPS 或来自地标观测的位置更新则将“world_frame”设置为“map_frame”的值。还要确保其他东西正在生成 odom-base_link 变换。这甚至可以是 robots_localization 状态估计节点的另一个实例。但是该实例不应融合全局数据。这是我们的案例因为我们使用 GPS 数据。 [sensor]对于每个传感器您需要根据传感器发布的主题定义此参数。 [sensor]_config由布尔 5×3 矩阵定义如下 bash [ X, Y, Z, Roll, Pitch, Yaw, dX/dt, dY/dt, dZ/dt, dRoll/dt, dPitch/dt, dYaw/dt, d2X/dt2, d2Y/dt2, d2Z/dt2]