# Learning-Aided 3D Mapping
[![Build Status](https://travis-ci.org/RobustFieldAutonomyLab/la3dm.svg?branch=master)](https://travis-ci.org/RobustFieldAutonomyLab/la3dm)
A suite of algorithms for learning-aided mapping. Includes implementations of Gaussian process regression and Bayesian generalized kernel inference for occupancy prediction using test-data octrees. A demonstration of the system can be found here: https://youtu.be/SRXLMALpU20
## Overview
This implementation as it stands now is primarily intended to enable replication of these methods over a few datasets. In addition to the implementation of relevant learning algorithms and data structures, we provide two sets of range data (sim_structured and sim_unstructured) collected in Gazebo for demonstration. Parameters of the sensors and environments are set in the relevant `yaml` files contained in the `config/datasets` directory, while configuration of parameters for the mapping methods can be found in `config/methods`.
## Getting Started
### Dependencies
The current package runs with ROS Noetic, but for testing in ROS Kinetic and ROS Indigo, you can set the CMAKE flag in the CMAKELists file to c++11.
Octomap is a dependancy, which can be installed using the command below. Change distribution as necessary.
```bash
$ sudo apt-get install ros-noetic-octomap*
```
### Building with catkin
The repository is set up to work with catkin, so to get started you can clone the repository into your catkin workspace `src` folder and compile with `catkin_make`:
```bash
my_catkin_workspace/src$ git clone https://github.com/RobustFieldAutonomyLab/la3dm.git
my_catkin_workspace/src$ cd ..
my_catkin_workspace$ catkin_make
my_catkin_workspace$ source ~/my_catkin_workspace/devel/setup.bash
```
## Running the Demo
To run the demo on the `sim_structured` environment, simply run:
```bash
$ roslaunch la3dm la3dm_static.launch
```
which by default will run using the BGKOctoMap-LV method. If you want to try a different method or dataset, simply pass the
name of the method or dataset as a parameter. For example, if you want to run GPOctoMap on the `sim_unstructured` map,
you would run:
```bash
$ roslaunch la3dm la3dm_static.launch method:=gpoctomap dataset:=sim_unstructured
```
## Relevant Publications
If you found this code useful, please cite the following:
Improving Obstacle Boundary Representations in Predictive Occupancy Mapping ([PDF](https://www.sciencedirect.com/science/article/abs/pii/S0921889022000380)) - describes the latest BGKOctoMap-LV addition to the LA3DM library:
```
@article{pearson2022improving,
title={Improving Obstacle Boundary Representations in Predictive Occupancy Mapping},
author={Pearson, Erik and Doherty, Kevin and Englot, Brendan},
journal={Robotics and Autonomous Systems},
volume={153},
pages={104077},
year={2022},
publisher={Elsevier}
}
```
Learning-Aided 3-D Occupancy Mapping with Bayesian Generalized Kernel Inference ([PDF](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8713569)) - describes the BGKOctoMap and BGKOctoMap-L approaches originally included in the LA3DM library.
```
@article{Doherty2019,
doi = {10.1109/tro.2019.2912487},
url = {https://doi.org/10.1109/tro.2019.2912487},
year = {2019},
publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
pages = {1--14},
author = {Kevin Doherty and Tixiao Shan and Jinkun Wang and Brendan Englot},
title = {Learning-Aided 3-D Occupancy Mapping With Bayesian Generalized Kernel Inference},
journal = {{IEEE} Transactions on Robotics}
}
```
Fast, accurate gaussian process occupancy maps via test-data octrees and nested Bayesian fusion ([PDF](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7487232)) - describes the GPOctoMap approach included in the LA3DM library.
```
@INPROCEEDINGS{JWang-ICRA-16,
author={J. Wang and B. Englot},
booktitle={2016 IEEE International Conference on Robotics and Automation (ICRA)},
title={Fast, accurate gaussian process occupancy maps via test-data octrees and nested Bayesian fusion},
year={2016},
pages={1003-1010},
month={May},
}
```
Bayesian Generalized Kernel Inference for Occupancy Map Prediction ([PDF](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7989356))
```
@INPROCEEDINGS{KDoherty-ICRA-17,
author={K. Doherty and J. Wang, and B. Englot},
booktitle={2017 IEEE International Conference on Robotics and Automation (ICRA)},
title={Bayesian Generalized Kernel Inference for Occupancy Map Prediction},
year={2017},
month={May},
}
```
## Contributors
Jinkun Wang, Kevin Doherty, and Erik Pearson, [Robust Field Autonomy Lab (RFAL)](https://robustfieldautonomylab.github.io/), Stevens Institute of Technology.
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学习辅助3D映射_C++_CMake_下载.zip (162个子文件)
sim_unstructured_h.bag 672KB
sim_unstructured.bag 672KB
sim_structured.bag 672KB
map.bt 64KB
map.bt 44KB
bgklvoctomap.cpp 21KB
bgkoctomap.cpp 20KB
gpoctomap.cpp 19KB
bgkloctomap.cpp 18KB
gpoctomap_server.cpp 8KB
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bgklvoctomap_server.cpp 7KB
bgklvblock.cpp 7KB
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bgkblock.cpp 7KB
gpblock.cpp 7KB
bgkloctomap_static_node.cpp 7KB
bgklvoctomap_static_node.cpp 6KB
gpoctomap_static_node.cpp 6KB
bgkoctomap_static_node.cpp 6KB
bgkloctree.cpp 5KB
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gpoctree.cpp 5KB
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gpoctree_node.cpp 2KB
bgkloctree_node.cpp 2KB
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point6f.cpp 2KB
point3f.cpp 2KB
sim_structured_octomap.csv 2.7MB
rtree.h 43KB
bgklvoctomap.h 16KB
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gpoctomap.h 15KB
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point6f.h 9KB
bgklinference.h 8KB
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markerarray_pub.h 6KB
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gpoctree_node.h 3KB
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settings.json 2KB
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la3dm_server.launch 960B
LICENSE 1KB
README.md 5KB
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