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Global Localization Using Object Detection with Semantic Map

Overview

Global localization is a key problem in autonomous robot. We use semantic map and object detection to do the global localization with MLE method.

Dependencies

• Jetpack=3.3
• TensorFlow=1.9.0
• Opencv
• Matplotlib
• PIL
• ROS Kinetic

Hardware

• Jetson TX2
• Lidar
• USB camera
• Autonomous robot
• Odometry by encoder or IMU

Motivation

• In ROS system, if we use move_base package, we need to input an 2D initial pose by hand:

• Therefore, we want to calculate the initial pose automatically.

How to Run

Object Detection Model

• train an object detection model using tensorflow
• export the frozen model, and put it into frozen_model folder
• put the whole package into a ROS workspace

Semantic Map

• we build a semantic map with Gmapping and object detection.
• the backgroud is the grid map, and the points in the map represent the object position.

ROS prepration

Before initial pose, you need to run the following node in ROS

• map server to output a map
• robot control: publish the cmd_vel and subscribe Odometry
• Lidar like Hokuyo, output the scan data

Global Localization

• Run python initial_pose.py in scripts folder.
• subscribe scan, imu/data topic, and need a USB camera
• publish cmd/vel to rotation, webcam_image, and the final initialpose

Other function

• camera_save.py: simple script to save the camera image
• visilize.py: an example script to test the frozen model with a video
• send_goal.cpp: we also provide a function which can send the navigation goal through voice recognition. Here we use the baidu package: https://github.com/DinnerHowe/baidu_speech
• center path: you need to alter the grid_path.cpp and input your own path in global planner package of navation stack.