{"id":28095275,"url":"https://github.com/robustfieldautonomylab/la3dm","last_synced_at":"2026-03-12T18:54:59.605Z","repository":{"id":47652245,"uuid":"79852322","full_name":"RobustFieldAutonomyLab/la3dm","owner":"RobustFieldAutonomyLab","description":"Learning-aided 3D mapping","archived":false,"fork":false,"pushed_at":"2023-11-24T02:07:35.000Z","size":2628,"stargazers_count":132,"open_issues_count":2,"forks_count":49,"subscribers_count":10,"default_branch":"master","last_synced_at":"2025-05-13T15:52:40.010Z","etag":null,"topics":["catkin","dataset","gaussian-process-occupancy-maps","gaussian-process-regression","gaussian-processes","mapping","occupancy-mapping","occupancy-prediction","octomap"],"latest_commit_sha":null,"homepage":"","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/RobustFieldAutonomyLab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2017-01-23T21:55:50.000Z","updated_at":"2025-04-22T18:04:26.000Z","dependencies_parsed_at":"2023-11-24T03:32:43.290Z","dependency_job_id":null,"html_url":"https://github.com/RobustFieldAutonomyLab/la3dm","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/RobustFieldAutonomyLab/la3dm","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RobustFieldAutonomyLab%2Fla3dm","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RobustFieldAutonomyLab%2Fla3dm/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RobustFieldAutonomyLab%2Fla3dm/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RobustFieldAutonomyLab%2Fla3dm/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/RobustFieldAutonomyLab","download_url":"https://codeload.github.com/RobustFieldAutonomyLab/la3dm/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RobustFieldAutonomyLab%2Fla3dm/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30438252,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-12T14:34:45.044Z","status":"ssl_error","status_checked_at":"2026-03-12T14:09:33.793Z","response_time":114,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["catkin","dataset","gaussian-process-occupancy-maps","gaussian-process-regression","gaussian-processes","mapping","occupancy-mapping","occupancy-prediction","octomap"],"created_at":"2025-05-13T15:39:48.488Z","updated_at":"2026-03-12T18:54:59.600Z","avatar_url":"https://github.com/RobustFieldAutonomyLab.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Learning-Aided 3D Mapping\n\nA 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\n\n## Overview\n\nThis 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`.\n\n## Getting Started\n\n### Dependencies\n\nThe 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.\n\nOctomap is a dependancy, which can be installed using the command below. Change distribution as necessary.\n\n```bash\n$ sudo apt-get install ros-noetic-octomap*\n```\n\n### Building with catkin\n\nThe 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`:\n\n```bash\nmy_catkin_workspace/src$ git clone https://github.com/RobustFieldAutonomyLab/la3dm.git\nmy_catkin_workspace/src$ cd ..\nmy_catkin_workspace$ catkin_make\nmy_catkin_workspace$ source ~/my_catkin_workspace/devel/setup.bash\n```\n\n## Running the Demo\n\nTo run the demo on the `sim_structured` environment, simply run:\n\n```bash\n$ roslaunch la3dm la3dm_static.launch\n```\n\nwhich by default will run using the BGKOctoMap-LV method. If you want to try a different method or dataset, simply pass the\nname of the method or dataset as a parameter. For example, if you want to run GPOctoMap on the `sim_unstructured` map,\nyou would run:\n\n```bash\n$ roslaunch la3dm la3dm_static.launch method:=gpoctomap dataset:=sim_unstructured\n```\n\n## Relevant Publications\n\nIf you found this code useful, please cite the following:\n\nImproving 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:\n\n```\n@article{pearson2022improving,\n  title={Improving Obstacle Boundary Representations in Predictive Occupancy Mapping},\n  author={Pearson, Erik and Doherty, Kevin and Englot, Brendan},\n  journal={Robotics and Autonomous Systems},\n  volume={153},\n  pages={104077},\n  year={2022},\n  publisher={Elsevier}\n}\n```\n\nLearning-Aided 3-D Occupancy Mapping with Bayesian Generalized Kernel Inference ([PDF](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=8713569)) - describes the BGKOctoMap and BGKOctoMap-L approaches originally included in the LA3DM library.\n```\n@article{Doherty2019,\n  doi = {10.1109/tro.2019.2912487},\n  url = {https://doi.org/10.1109/tro.2019.2912487},\n  year = {2019},\n  publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},\n  pages = {1--14},\n  author = {Kevin Doherty and Tixiao Shan and Jinkun Wang and Brendan Englot},\n  title = {Learning-Aided 3-D Occupancy Mapping With Bayesian Generalized Kernel Inference},\n  journal = {{IEEE} Transactions on Robotics}\n}\n```\n\nFast, accurate gaussian process occupancy maps via test-data octrees and nested Bayesian fusion ([PDF](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=7487232)) - describes the GPOctoMap approach included in the LA3DM library.\n```\n@INPROCEEDINGS{JWang-ICRA-16,\nauthor={J. Wang and B. Englot},\nbooktitle={2016 IEEE International Conference on Robotics and Automation (ICRA)},\ntitle={Fast, accurate gaussian process occupancy maps via test-data octrees and nested Bayesian fusion},\nyear={2016},\npages={1003-1010},\nmonth={May},\n}\n```\n\nBayesian Generalized Kernel Inference for Occupancy Map Prediction ([PDF](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=7989356))\n```\n@INPROCEEDINGS{KDoherty-ICRA-17,\nauthor={K. Doherty and J. Wang, and B. Englot},\nbooktitle={2017 IEEE International Conference on Robotics and Automation (ICRA)},\ntitle={Bayesian Generalized Kernel Inference for Occupancy Map Prediction},\nyear={2017},\nmonth={May},\n}\n```\n\n## Contributors\n\nJinkun Wang, Kevin Doherty, and Erik Pearson, [Robust Field Autonomy Lab (RFAL)](https://robustfieldautonomylab.github.io/), Stevens Institute of Technology.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frobustfieldautonomylab%2Fla3dm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frobustfieldautonomylab%2Fla3dm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frobustfieldautonomylab%2Fla3dm/lists"}