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Wall Localization Demo

Overview

This folder contains the code developed for wall-based scanner localization using geometric and appearance-based matching. The approach localizes a vertical profiler or scanner relative to a known wall geometry by combining 2D image-based matching (LBP or SIFT) with 3D ICP refinement.

Author: Linh Thach Dang
E-Mail: linh.thach.dang@stud.uni-hannover.de

Installation

Copy this folder into the src directory of a catkin workspace and build it using catkin_tools.

cd ~/catkin_ws/src
cp -r wall_localization_demo .
cd ~/catkin_ws
catkin build
source devel/setup.bash

Add the folder data from the additional thesis files and move the wall_mesh.obj from data to models/printed wall. To correctly spawn the wall in gazebo, add the following line to your ~/.bashrc:

export GAZEBO_MODEL_PATH=$GAZEBO_MODEL_PATH:~/catkin_ws/src/wall_localization_demo/models

Package

wall_localization_demo

This package contains all nodes, scripts, and launch files required for:

  • offline wall map generation,
  • live localization,
  • evaluation against ground truth.

Scripts

All core scripts are located in src/wall_localization_demo/.

main.py

Runs the live localization pipeline.
Subscribes to a LaserScan topic, transforms scan points into the wall frame, builds a local patch image, performs 2D localization (SIFT or LBP), refines the pose using ICP, and publishes the estimated scanner pose as the TF frame scanner_icp_refined.

localization_core.py

Implements the core localization algorithms used by main.py, including patch generation, descriptor extraction, database matching, ICP refinement, and pose estimation.

compare_poses.py

Logs ground truth and estimated poses during localization.
Samples TF and writes two CSV files:

  • groundtruth_poses.csv (wall → vertical_profiler_link)
  • estimated_poses.csv (wall → scanner_icp_refined)

move_along_wall.py

Moves the robot along the wall at a fixed offset to generate continuous scan data during experiments.

pcl2texture.py

Utility script to convert point cloud data into image or texture representations of the wall.