This package is for running the semantic SLAM algorithm using extracted planar surfaces from the received detection

Overview

Semantic SLAM

This package can perform optimization of pose estimated from VO/VIO methods which tend to drift over time. It uses planar surfaces extracted from object detections in order to create a sparse semantic map of the environment, thus optimizing the drift of the VO/VIO algorithms.

In order to run this package you will need two additional modules

Currently it can extract planar surfaces and create a semantic map from from the following objects:

  • chair
  • tvmonitor
  • book
  • keyboard
  • laptop
  • bucket
  • car

Related Paper:

@ARTICLE{9045978,
  author={Bavle, Hriday and De La Puente, Paloma and How, Jonathan P. and Campoy, Pascual},
  journal={IEEE Access}, 
  title={VPS-SLAM: Visual Planar Semantic SLAM for Aerial Robotic Systems}, 
  year={2020},
  volume={8},
  number={},
  pages={60704-60718},
  doi={10.1109/ACCESS.2020.2983121}}

Video

Semantic SLAM

How do I set it up?

First install g2o following these instructions (Tested on Kinetic and Melodic Distributions):

- sudo apt-get install ros-$ROS_DISTRO-libg2o
- sudo cp -r /opt/ros/$ROS_DISTRO/lib/libg2o_* /usr/local/lib
- sudo cp -r /opt/ros/$ROS_DISTRO/include/g2o /usr/local/include

Install OctopMap server for map generation capabilities:

- sudo apt install ros-$ROS_DISTRO-octomap*

Try a simple example with pre-recorded VIO pose and a blue bucket detector:

Create a ros workspace and clone the following packages:

  • Download the rosbag:
    wget -P ~/Downloads/ https://www.dropbox.com/s/jnywuvcn2m9ubu2/entire_lab_3_rounds.bag
  • Create a workspace, clone the repo and compile:
    mkdir -p workspace/ros/semantic_slam_ws/src/ && cd workspace/ros/semantic_slam_ws/src/    
    git clone https://github.com/hridaybavle/semantic_slam && git clone https://bitbucket.org/hridaybavle/bucket_detector.git   
    cd .. && catkin build --cmake-args -DCMAKE_BUILD_TYPE=Release
  • Launch and visualize
    source devel/setup.bash
    roslaunch semantic_SLAM ps_slam_with_snap_pose_bucket_det_lab_data_with_octomap.launch bagfile:=${HOME}/Downloads/entire_lab_3_rounds.bag show_rviz:=true  

test

Using Docker Image

If the code is giving problems with you local machine, you can try the docker image created with the repo and the required settings.

Download Docker from: Docker

Follow the commands to run the algorithm with the docker

  docker pull hridaybavle/semantic_slam:v1 	
  docker run --rm -it --net="host" -p 11311:11311 hridaybavle/semantic_slam:v1 bash
  cd ~/workspace/ros/semantic_slam_ws/
  source devel/setup.bash
  roslaunch semantic_SLAM ps_slam_with_snap_pose_bucket_det_lab_data_with_octomap.launch bagfile:=${HOME}/Downloads/entire_lab_3_rounds.bag show_rviz:=false  

Open a new terminal and rviz in local machine

  cd ~/Downloads/ && wget https://raw.githubusercontent.com/hridaybavle/semantic_slam/master/rviz/graph_semantic_slam.rviz
  rviz -d graph_semantic_slam.rviz	

Subsribed Topics

Published Topics

The configurations of the algorithms can be found inside the cfg folder in order to be changed accordingly.

Published TFs

  • map to odom transform: The transform published between the map frame and the odom frame after the corrections from the semantic SLAM.

  • base_link to odom transform: The transform published between the base_link (on the robot) frame and the odom frame as estimated by the VO/VIO algorithm.

You might also like...
Indoor Panorama Planar 3D Reconstruction via Divide and Conquer
Indoor Panorama Planar 3D Reconstruction via Divide and Conquer

HV-plane reconstruction from a single 360 image Code for our paper in CVPR 2021: Indoor Panorama Planar 3D Reconstruction via Divide and Conquer (pape

[ICCV 2021 (oral)] Planar Surface Reconstruction from Sparse Views
[ICCV 2021 (oral)] Planar Surface Reconstruction from Sparse Views

Planar Surface Reconstruction From Sparse Views Linyi Jin, Shengyi Qian, Andrew Owens, David F. Fouhey University of Michigan ICCV 2021 (Oral) This re

PyTorch implementation of HDN(Homography Decomposition Networks) for planar object tracking
PyTorch implementation of HDN(Homography Decomposition Networks) for planar object tracking

Homography Decomposition Networks for Planar Object Tracking This project is the offical PyTorch implementation of HDN(Homography Decomposition Networ

Pytorch implementation of paper:
Pytorch implementation of paper: "NeurMiPs: Neural Mixture of Planar Experts for View Synthesis"

NeurMips: Neural Mixture of Planar Experts for View Synthesis This is the official repo for PyTorch implementation of paper "NeurMips: Neural Mixture

Sequence lineage information extracted from RKI sequence data repo
Sequence lineage information extracted from RKI sequence data repo

Pango lineage information for German SARS-CoV-2 sequences This repository contains a join of the metadata and pango lineage tables of all German SARS-

Official page of Struct-MDC (RA-L'22 with IROS'22 option); Depth completion from Visual-SLAM using point & line features
Official page of Struct-MDC (RA-L'22 with IROS'22 option); Depth completion from Visual-SLAM using point & line features

Struct-MDC (click the above buttons for redirection!) Official page of "Struct-MDC: Mesh-Refined Unsupervised Depth Completion Leveraging Structural R

Code for
Code for "Diffusion is All You Need for Learning on Surfaces"

Source code for "Diffusion is All You Need for Learning on Surfaces", by Nicholas Sharp Souhaib Attaiki Keenan Crane Maks Ovsjanikov NOTE: the linked

Code for Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations
Code for Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations

Implementation for Iso-Points (CVPR 2021) Official code for paper Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations paper |

 Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces(ICML 2021)
Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces(ICML 2021)

Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces(ICML 2021) This repository contains the code

Comments
  • errors at last step

    errors at last step

    Hi, I have finished all the steps following the instructions and nothing goes wrong. But when I run

    roslaunch semantic_SLAM ps_slam_with_snap_pose_bucket_det_lab_data.launch bagfile:=${HOME}/Downloads/entire_lab_3_rounds.bag show_rviz:=true  
    

    I get errors like this and it stucks for a while.

    # Using CSparse poseDim -1 landMarkDim -1 blockordering 0
    done
    keyframe_delta_trans 0.5
    keyframe_delta_angle 0.5
    keyframe_delta_time 1
    use_const_inf_matrix: 1
    const_stddev_x: 0.00667
    const_stddev_q: 1e-05
    Initialized mapping thread 
    camera angle in radians: 0.59219
    update keyframe every detection: 1
    add first landmark: 0
    [semantic_graph_slam_node-9] process has died [pid 23067, exit code -11, cmd /home/nrc/workspace/ros/semantic_slam_ws/devel/lib/semantic_SLAM/semantic_graph_SLAM_node __name:=semantic_graph_slam_node __log:=/home/nrc/.ros/log/ccaf4b14-a47a-11ea-b300-000c29c39525/semantic_graph_slam_node-9.log].
    log file: /home/nrc/.ros/log/ccaf4b14-a47a-11ea-b300-000c29c39525/semantic_graph_slam_node-9*.log
    

    then I get this. It seems that the visualization program doesn't go right.

    [rosbag-2] process has finished cleanly
    log file: /home/nrc/.ros/log/ccaf4b14-a47a-11ea-b300-000c29c39525/rosbag-2*.log
    

    Is there something I have missed? Thank you!

    opened by ZhengXinyue 8
  • [semantic_graph_slam_node-9] process has died

    [semantic_graph_slam_node-9] process has died

    Hi, I have finished all the steps following the instructions and nothing goes wrong. But when I run

    roslaunch semantic_SLAM ps_slam_with_snap_pose_bucket_det_lab_data_with_octomap.launch bagfile:=${HOME}/Downloads/entire_lab_3_rounds.bag show_rviz:=true
    

    I get errors like this.

    done
    keyframe_delta_trans 0.5
    keyframe_delta_angle 0.5
    keyframe_delta_time 1
    use_const_inf_matrix: 1
    const_stddev_x: 0.00667
    const_stddev_q: 1e-05
    camera angle in radians: 0.59219
    update keyframe every detection: 1
    add first landmark: 0
    [ INFO] [1591944956.099907360, 1661396775.076756992]: waitForService: Service [/depth_rectifier_manager/load_nodelet] is now available.
    [ INFO] [1591944956.100243666, 1661396775.076756992]: waitForService: Service [/depth_manager/load_nodelet] is now available.
    [ INFO] [1591944956.545617511, 1661396775.518832629]: Stereo is NOT SUPPORTED
    [ INFO] [1591944956.545842654, 1661396775.518832629]: OpenGl version: 4.5 (GLSL 4.5).
    [pcl::OrganizedNeighbor::radiusSearch] Input dataset is not from a projective device!
    Residual (MSE) 0.000614, using 1248 valid points
    [pcl::OrganizedNeighbor::radiusSearch] Input dataset is not from a projective device!
    Residual (MSE) 0.000748, using 1444 valid points
    [pcl::OrganizedNeighbor::radiusSearch] Input dataset is not from a projective device!
    Residual (MSE) 0.001710, using 2303 valid points
    [semantic_graph_slam_node-9] process has died [pid 27314, exit code -9, cmd /home/nrc/hd/workspace/ros/semantic_slam_ws/devel/lib/semantic_SLAM/semantic_graph_SLAM_node __name:=semantic_graph_slam_node __log:=/home/nrc/.ros/log/c2c4ddd8-ac79-11ea-96ed-8ca982ff1833/semantic_graph_slam_node-9.log].
    log file: /home/nrc/.ros/log/c2c4ddd8-ac79-11ea-96ed-8ca982ff1833/semantic_graph_slam_node-9*.log
    

    When it occurs

    [pcl::OrganizedNeighbor::radiusSearch] Input dataset is not from a projective device!
    Residual (MSE) 0.000614, using 1248 valid points
    

    the program is still mapping , so I think the problem is not caused by 'pcl'.

    I tried to run the launchfile seperately :

    ROS_NAMESPACE=camera/color rosrun image_proc image_proc 
    roslaunch semantic_SLAM shape.launch  
    rosrun semantic_SLAM  semantic_graph_SLAM_node
    

    But at the last step i got 'Segmentation fault :

    add first landmark: 0
    Segmentation fault (core dumped)
    

    Do you have any idea about it? Thanks a lot !!!

    opened by He-Rong 6
  • Dataset download failure problem

    Dataset download failure problem

    Hello, when I run the sample code, I always encounter network interruptions or unknown errors at the last moment when downloading the dataset entire_lab_3_rounds.bag. Can you provide a new way to download the bag?

    opened by kycwx 2
  • Problemas de incompatibilidad de opencv en el bucket detector

    Problemas de incompatibilidad de opencv en el bucket detector

    Hola, he conseguido que ambos paquetes en conjunto (semantic slam y bucket detector) funciones bien en una distro de ubuntu virgen con ROS melodic, sin embargo, cuando migro al pc donde trabajo habitualmente y que tiene ya instaladas dependencias anteriores y demás me encuentro con estos errores referentes a opencv: Captura de pantalla de 2021-05-26 11-29-18 Imagino que se deben a incompatibilidades entre versiones de opencv, podrías confirmarme esto último? Sería posible trabajar con una versión de opencv diferente? Gracias, un saludo!

    opened by iandresolares 2
Releases(2.0.0)
Owner
Hriday Bavle
Postdoctoral Researcher at the University of Luxembourg. My research interests are VO/VIO, SLAM, Perception and Planning applied to Mobile Robots.
Hriday Bavle
Tensors and Dynamic neural networks in Python with strong GPU acceleration

PyTorch is a Python package that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration Deep neural networks b

61.4k Jan 04, 2023
A Pytorch Implementation for Compact Bilinear Pooling.

CompactBilinearPooling-Pytorch A Pytorch Implementation for Compact Bilinear Pooling. Adapted from tensorflow_compact_bilinear_pooling Prerequisites I

169 Dec 23, 2022
[NeurIPS 2021] SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning

SSUL - Official Pytorch Implementation (NeurIPS 2021) SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning Sun

Clova AI Research 44 Dec 27, 2022
CenterPoint 3D Object Detection and Tracking using center points in the bird-eye view.

CenterPoint 3D Object Detection and Tracking using center points in the bird-eye view. Center-based 3D Object Detection and Tracking, Tianwei Yin, Xin

Tianwei Yin 134 Dec 23, 2022
Indonesian Car License Plate Character Recognition using Tensorflow, Keras and OpenCV.

Monopol Indonesian Car License Plate (Indonesia Mobil Nomor Polisi) Character Recognition using Tensorflow, Keras and OpenCV. Background This applicat

Jayaku Briliantio 3 Apr 07, 2022
Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch

Enformer - Pytorch (wip) Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch. The original tensorflow

Phil Wang 235 Dec 27, 2022
Pytorch Lightning Distributed Accelerators using Ray

Distributed PyTorch Lightning Training on Ray This library adds new PyTorch Lightning plugins for distributed training using the Ray distributed compu

167 Jan 02, 2023
A generalist algorithm for cell and nucleus segmentation.

Cellpose | A generalist algorithm for cell and nucleus segmentation. Cellpose was written by Carsen Stringer and Marius Pachitariu. To learn about Cel

MouseLand 733 Dec 29, 2022
Example repository for custom C++/CUDA operators for TorchScript

Custom TorchScript Operators Example This repository contains examples for writing, compiling and using custom TorchScript operators. See here for the

106 Dec 14, 2022
Space Invaders For Python

Space-Invaders Just download or clone the git repository. To run the Space Invader game you need to have pyhton installed in you system. If you dont h

Fei 5 Jul 27, 2022
An all-in-one application to visualize multiple different local path planning algorithms

Table of Contents Table of Contents Local Planner Visualization Project (LPVP) Features Installation/Usage Local Planners Probabilistic Roadmap (PRM)

Abdur Javaid 47 Dec 30, 2022
Python3 Implementation of (Subspace Constrained) Mean Shift Algorithm in Euclidean and Directional Product Spaces

(Subspace Constrained) Mean Shift Algorithms in Euclidean and/or Directional Product Spaces This repository contains Python3 code for the mean shift a

Yikun Zhang 0 Oct 19, 2021
This is a project based on ConvNets used to identify whether a road is clean or dirty. We have used MobileNet as our base architecture and the weights are based on imagenet.

PROJECT TITLE: CLEAN/DIRTY ROAD DETECTION USING TRANSFER LEARNING Description: This is a project based on ConvNets used to identify whether a road is

Faizal Karim 3 Nov 06, 2022
PyTorch DepthNet Training on Still Box dataset

DepthNet training on Still Box Project page This code can replicate the results of our paper that was published in UAVg-17. If you use this repo in yo

Clément Pinard 115 Nov 21, 2022
Implementation of the paper "Fine-Tuning Transformers: Vocabulary Transfer"

Transformer-vocabulary-transfer Implementation of the paper "Fine-Tuning Transfo

LEYA 13 Nov 30, 2022
Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel

Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel This repository is the official PyTorch implementation of BSRDM w

Zongsheng Yue 69 Jan 05, 2023
StyleGAN2-ADA-training-jupyter - Training custom datasets in styleGAN2-ADA by NVIDIA using Jupyter

styleGAN2-ADA-training-jupyter Training custom datasets in styleGAN2-ADA on Jupyter Official StyleGAN2-ADA by NIVIDIA Paper Training Generative Advers

Mang Su Hyun 2 Feb 24, 2022
STBP is a way to train SNN with datasets by Backward propagation.

Spiking neural network (SNN), compared with depth neural network (DNN), has faster processing speed, lower energy consumption and more biological interpretability, which is expected to approach Stron

Ling Zhang 18 Dec 09, 2022
ALBERT-pytorch-implementation - ALBERT pytorch implementation

ALBERT-pytorch-implementation developing... 모델의 개념이해를 돕기 위한 구현물로 현재 변수명을 상세히 적었고

BG Kim 3 Oct 06, 2022
Neural Oblivious Decision Ensembles

Neural Oblivious Decision Ensembles A supplementary code for anonymous ICLR 2020 submission. What does it do? It learns deep ensembles of oblivious di

25 Sep 21, 2022