HI, i am trying to fuse my sensors and rtabmap visual odom topics together. However, robot_localization doesnt output anything. The diagnostic topic also doesnt tells me anything. Here is a link to my rosbag

launching rtabmap : rosrun rtabmap_ros rgbd_odometry rgb/image:=/stereo_inertial_publisher/left/image_rect depth/image:=/stereo_inertial_publisher/stereo/depth rgb/camera_info:=/stereo_inertial_publisher/left/camera_info _frame_id:=oak-d_frame

launching robot_localization: roslaunch robot_localization ekf_local.launch viz_veh:=false rviz:=false

Here is my yaml

frequency: 30 two_d_mode: true transform_time_offset: 0.0 transform_timeout: 0.02 print_diagnostics: true debug: false debug_out_file: /home/catkin_ws/src/debug.txt publish_tf: false publish_acceleration: false map_frame: map # Defaults to "map" if unspecified odom_frame: odom # Defaults to "odom" if unspecified base_link_frame: r_robot # Defaults to "base_link" if unspecified world_frame: odom # Defaults to the value of odom_frame if unspecified

odom0: /odometry/filtered odom0_config: [false, false, false, # x y z true, false, false, # roll pitch yaw false, true, false, # vx vy vz false, false, true, # vroll vpitch vyaw false, false, false] # ax ay az odom0_queue_size: 10 odom0_nodelay: false odom0_differential: false odom0_relative: false

odom1: /odom_rgbd_image odom1_config: [false, false, false, # x y z false, false, false, # roll pitch yaw true, true, false, # vx vy vz false, false, true, # vroll vpitch vyaw false, false, false] # ax ay az

odom1_queue_size: 10 odom1_nodelay: false odom1_differential: false odom1_relative: false

use_control: false stamped_control: false control_timeout: 0.2 control_config: [true, false, false, false, false, true] 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 ...