I have created a map using hectorslam, loaded the map via mapserver. Run my odometry and amcl for localization, at first the laserscan aligns with the map but then when i move the robot using teleoptwistkeyboard the laserscan gets mis aligned. Is this suppose to act this way? I tried changing the Fixed Frame from(laser,map,odom,base_link) and all has the same result. Can anyone identify whats causing the problem? Thanks in advance :)

ROS melodic ROS Master: Raspberry pi 4 ROS Slave: Raspberry pi 4

Master terminal:

1.roscore
2.mimi.launch

Slave terminal:

mimi.launch

Ros Master Launch file:

<launch>

    <node pkg="amcl" type="amcl" name="amcl" output="screen">
       <!-- Publish scans from best pose at a max of 10 Hz -->
      <param name="odom_model_type" value="diff"/>
      <param name="odom_alpha5" value="0.1"/>
      <param name="transform_tolerance" value="0.2" />
      <param name="gui_publish_rate" value="10.0"/>
      <param name="laser_max_beams" value="30"/>
      <param name="min_particles" value="500"/>
      <param name="max_particles" value="5000"/>
      <param name="kld_err" value="0.05"/>
      <param name="kld_z" value="0.99"/>
      <param name="odom_alpha1" value="0.2"/>
      <param name="odom_alpha2" value="0.2"/>
      <!-- translation std dev, m -->
      <param name="odom_alpha3" value="0.8"/>
      <param name="odom_alpha4" value="0.2"/>
      <param name="laser_z_hit" value="0.5"/>
      <param name="laser_z_short" value="0.05"/>
      <param name="laser_z_max" value="0.05"/>
      <param name="laser_z_rand" value="0.5"/>
      <param name="laser_sigma_hit" value="0.2"/>
      <param name="laser_lambda_short" value="0.1"/>
      <param name="laser_lambda_short" value="0.1"/>
      <param name="laser_model_type" value="likelihood_field"/>
      <!-- <param name="laser_model_type" value="beam"/> -->
      <param name="laser_likelihood_max_dist" value="2.0"/>
      <param name="update_min_d" value="0.2"/>
      <param name="update_min_a" value="0.5"/>
      <param name="odom_frame_id" value="odom"/>
      <param name="resample_interval" value="1"/>
      <param name="transform_tolerance" value="0.2"/>
      <param name="recovery_alpha_slow" value="0.0"/>
      <param name="recovery_alpha_fast" value="0.0"/>
    </node>
    <!--node pkg="tf" type="static_transform_publisher" name="base_laser" args="0 0 0 0 0 0 base_link laser 100"/-->
    <node pkg="teleop_twist_keyboard" name="teleop_twist_keyboard" type="teleop_twist_keyboard.py" output="screen"/> 
    <node pkg="rviz" type="rviz" name="rviz"/>

</launch>

Ros Slave Launch file:

 <launch>
  <node name="rplidarNode"          pkg="rplidar_ros"  type="rplidarNode" output="screen">
  <param name="serial_port"         type="string" value="/dev/ttyUSB0"/>
  <param name="serial_baudrate"     type="int"    value="115200"/><!--A1/A2 -->
  <!--param name="serial_baudrate"     type="int"    value="256000"--><!--A3 -->
  <param name="frame_id"            type="string" value="laser"/>
  <param name="inverted"            type="bool"   value="false"/>
  <param name="angle_compensate"    type="bool"   value="true"/>
  </node>
<node pkg="mimi_2dnav" name="odom" type="odom.py" output="screen"/>
<node pkg="mimi_2dnav" name="subscrib" type="subscrib.py" output="screen"/>
<node pkg="map_server" type="map_server" name="map_server" args="/home/pi/catkin_workspace/src/navigation/map_server/m.yaml" />
<node pkg="tf" type="static_transform_publisher" name="base_link_to_laser" args="0.0 0.0 0.15 -0.1 0.0 0.0 base_link laser 100" />
</launch>

Odometry publisher:

#!/usr/bin/env python
import math
from math import sin, cos, pi
import RPi.GPIO as GPIO
import rospy
import numpy as np
import tf
from nav_msgs.msg import Odometry
from geometry_msgs.msg import Point, Pose, Quaternion, Twist, Vector3
rospy.init_node('odometry_publisher')
odom_pub = rospy.Publisher("odom", Odometry, queue_size=50)
odom_broadcaster = tf.TransformBroadcaster()
GPIO.setwarnings(False)
GPIO.setmode(GPIO.BOARD)
GPIO.setup(13, GPIO.IN, pull_up_down=GPIO.PUD_DOWN)
GPIO.setup(12, GPIO.IN, pull_up_down=GPIO.PUD_DOWN)
right_enc=0
left_enc=0
temp_right_enc=0
temp_left_enc=0
def handle1(self):
    global right_enc
    right_enc=right_enc+1
def handle2(self):
    global left_enc
    left_enc=left_enc+1 
GPIO.add_event_detect(13, GPIO.RISING, handle1)
GPIO.add_event_detect(12, GPIO.RISING, handle2)
x = np.float32(0.0)
y = np.float32(0.0)
th = np.float32(0.0)
dist_bet_wheels=.155
vy = np.float32(-0.1)
current_time = rospy.Time.now()
last_time = rospy.Time.now()
r = rospy.Rate(10.0)
while not rospy.is_shutdown():
    getting encoder ticks
    left_dTick=left_enc-temp_left_enc
    right_dTick=right_enc-temp_right_enc
    temp_left_enc=left_enc
    temp_right_enc=right_enc

    #computing travel distance of each wheel
    dL=0.054*pi*(left_dTick/12.0)
    dR=0.054*pi*(right_dTick/12.0)

    # compute odometry in a typical way given the velocities of the robot
    current_time = rospy.Time.now()
    dt = (current_time - last_time).to_sec()
    last_time = current_time
    vL=dL/dt
    vR=dR/dt
    vth = (vR - vL)/ dist_bet_wheels
    vx= (dR + dL)/ 2.0
    vy=0
    delta_x = ((vx * cos(th)) - (vy * sin(th))) * dt
    delta_y = ((vx * sin(th)) + (vy * cos(th))) * dt
    delta_th = vth * dt

    x += delta_x
    y += delta_y
    th += delta_th


    # since all odometry is 6DOF we'll need a quaternion created from yaw
    odom_quat = tf.transformations.quaternion_from_euler(0, 0, th)

    # first, we'll publish the transform over tf
    odom_broadcaster.sendTransform(
        (x, y, 0.0),
        odom_quat,
        current_time,
        "base_link",
        "odom"
    )

    # next, we'll publish the odometry message over ROS
    odom = Odometry()
    odom.header.stamp = current_time
    odom.header.frame_id = "odom"

    # set the position
    odom.pose.pose = Pose(Point(x, y, 0.0), Quaternion(*odom_quat))

    # set the velocity
    odom.child_frame_id = "base_link"
    odom.twist.twist = Twist(Vector3(vx, vy, 0), Vector3(0, 0, vth))
    # publish the message
    odom_pub.publish(odom)   
    r.sleep()

Screenshots: rqt_graph:

tf view_frames:

before moving:

after moving: