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https://github.com/mit-spark/kimera-vio

Visual Inertial Odometry with SLAM capabilities and 3D Mesh generation.
https://github.com/mit-spark/kimera-vio

cpu euroc-dataset localization mapping real-time reconstruction robotics slam state-estimation vio visual-inertial-odometry

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Visual Inertial Odometry with SLAM capabilities and 3D Mesh generation.

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README 

        


  

    sparklab

   

  

    kimera

   

   

    mit

  




# Kimera-VIO: Open-Source Visual Inertial Odometry



[![Build Status](http://ci-sparklab.mit.edu:8080/job/MIT-SPARK-Kimera/job/master/badge/icon)](http://ci-sparklab.mit.edu:8080/job/MIT-SPARK-Kimera/job/master/)

For evaluation plots, check our [jenkins server](http://ci-sparklab.mit.edu:8080/job/MIT-SPARK-Kimera/job/master/VIO_20Euroc_20Performance_20Report).



**Authors:** [Antoni Rosinol](https://www.mit.edu/~arosinol/), Yun Chang, Marcus Abate, Nathan Hughes, Sandro Berchier, [Luca Carlone](https://lucacarlone.mit.edu/)



## What is Kimera-VIO?



Kimera-VIO is a Visual Inertial Odometry pipeline for accurate State Estimation from **Stereo** + **IMU** data. It can optionally use **Mono** + **IMU** data instead of stereo cameras.



## Publications



We kindly ask to cite our paper if you find this library useful:



- A. Rosinol, M. Abate, Y. Chang, L. Carlone, [**Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping**](https://arxiv.org/abs/1910.02490). IEEE Intl. Conf. on Robotics and Automation (ICRA), 2020. [arXiv:1910.02490](https://arxiv.org/abs/1910.02490).

 

 ```bibtex

 @InProceedings{Rosinol20icra-Kimera,

   title = {Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping},

   author = {Rosinol, Antoni and Abate, Marcus and Chang, Yun and Carlone, Luca},

   year = {2020},

   booktitle = {IEEE Intl. Conf. on Robotics and Automation (ICRA)},

   url = {https://github.com/MIT-SPARK/Kimera},

   pdf = {https://arxiv.org/pdf/1910.02490.pdf}

 }

 ```

 

 - A. Rosinol and A. Violette and M. Abate and N. Hughes and Y. Chang and J. Shi and A. Gupta and L. Carlone, [**Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs**](https://arxiv.org/abs/2101.06894). arXiv preprint, 2021. [arXiv:2101.06894](https://arxiv.org/abs/2101.06894).



```bibtex

@article{Rosinol21arxiv-Kimera,

   title = {Kimera: from {SLAM} to Spatial Perception with {3D} Dynamic Scene Graphs},

   author = {Rosinol, Antoni and Violette, Andrew and Abate, Marcus and Hughes, Nathan and Chang, Yun

   and Shi, Jingnan and Gupta, Arjun and Carlone, Luca},

   year = {2021},

   journal = {arXiv preprint arXiv: 2101.06894},

   pdf = {https://arxiv.org/pdf/2101.06894.pdf} 

}

```



### Related Publications



Backend optimization is based on:



 - C. Forster, L. Carlone, F. Dellaert, and D. Scaramuzza. [**On-Manifold Preintegration Theory for Fast and Accurate Visual-Inertial Navigation**](https://arxiv.org/pdf/1512.02363.pdf). IEEE Trans. Robotics, 33(1):1-21, 2016.



 - L. Carlone, Z. Kira, C. Beall, V. Indelman, and F. Dellaert. [**Eliminating Conditionally Independent Sets in Factor Graphs: A Unifying Perspective based on Smart Factors.**](https://www.cc.gatech.edu/~dellaert/dhtml/pubs/Carlone14icra.pdf) IEEE Intl. Conf. on Robotics and Automation (ICRA), 2014.



Alternatively, the `Regular VIO` Backend, using structural regularities, is described in this paper:



- A. Rosinol, T. Sattler, M. Pollefeys, and L. Carlone. [**Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities**](https://arxiv.org/pdf/1903.01067.pdf). IEEE Int. Conf. on Robotics and Automation (ICRA), 2019.



## Demo





  




# 1. Installation



Tested on Ubuntu 20.04.



## Prerequisites



- [GTSAM](https://github.com/borglab/gtsam) >= 4.1

- [OpenCV](https://github.com/opencv/opencv) >= 3.4

- [OpenGV](https://github.com/laurentkneip/opengv)

- [Glog](http://rpg.ifi.uzh.ch/docs/glog.html), [Gflags](https://gflags.github.io/gflags/)

- [Gtest](https://github.com/google/googletest/blob/master/googletest/docs/primer.md) (installed automagically).

- [DBoW2](https://github.com/dorian3d/DBoW2)

- [Kimera-RPGO](https://github.com/MIT-SPARK/Kimera-RPGO)

- [ANMS](https://github.com/BAILOOL/ANMS-Codes) (source files in `src/frontend/feature-detector/anms`, used for adaptive non-max suppression).



> Note: if you want to avoid building all dependencies yourself, we provide a docker image that will install them for you. Check installation instructions in [docs/kimera_vio_install.md](./docs/kimera_vio_install.md).



> Note 2: if you use ROS, then [Kimera-VIO-ROS](https://github.com/MIT-SPARK/Kimera-VIO-ROS) can install all dependencies and Kimera inside a catkin workspace.



## Installation Instructions



Find how to install Kimera-VIO and its dependencies here: **[Installation instructions](./docs/kimera_vio_install.md)**.



# 2. Usage



## General tips



The LoopClosureDetector (and PGO) module is disabled by default. If you wish to run the pipeline with loop-closure detection enabled, set the `use_lcd` flag to true. For the example script, this is done by passing `-lcd` at commandline like so:

```bash

./scripts/stereoVIOEUROC.bash -lcd

```



To log output, set the `log_output` flag to true. For the script, this is done with the `-log` commandline argument. By default, log files will be saved in [output_logs](output_logs/).



To run the pipeline in sequential mode (one thread only), set `parallel_run`to false. This can be done in the example script with the `-s` argument at commandline.



## i. [Euroc](http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets) Dataset



#### Download Euroc's dataset



- Download one of Euroc's datasets, for example [V1_01_easy.zip](http://robotics.ethz.ch/~asl-datasets/ijrr_euroc_mav_dataset/vicon_room1/V1_01_easy/V1_01_easy.zip).



> Datasets MH_04 and V2_03 have different number of left/right frames. We suggest using instead [**our version of Euroc here**](https://drive.google.com/open?id=1_kwqHojvBusHxilcclqXh6haxelhJW0O).



- Unzip the dataset to your preferred directory, for example, in `~/Euroc/V1_01_easy`:

```bash

mkdir -p ~/Euroc/V1_01_easy

unzip -o ~/Downloads/V1_01_easy.zip -d ~/Euroc/V1_01_easy

```



#### Yamelize Euroc's dataset



Add `%YAML:1.0` at the top of each `.yaml` file inside Euroc.

You can do this manually or run the `yamelize.bash` script by indicating where the dataset is (it is assumed below to be in `~/path/to/euroc`):

> You don't need to yamelize the dataset if you download our version [here](https://drive.google.com/open?id=1_kwqHojvBusHxilcclqXh6haxelhJW0O)

```bash

cd Kimera-VIO

bash ./scripts/euroc/yamelize.bash -p ~/path/to/euroc

```



### Run Kimera-VIO in Euroc's dataset



Using a bash script bundling all command-line options and gflags:



```bash

cd Kimera-VIO

bash ./scripts/stereoVIOEuroc.bash -p "PATH_TO_DATASET/V1_01_easy"

```



> Alternatively, one may directly use the executable in the build folder:

`./build/stereoVIOEuroc`. Nevertheless, check the script `./scripts/stereoVIOEuroc.bash` to understand what parameters are expected, or check the [parameters](#Parameters) section below.



Kimera can also run in monocular mode. For Euroc, this means only processing the left image. To use this simply use the parameters in `params/EurocMono`. In the bash script there is a `PARAMS_PATH` variable that can be set to point to these parameters instead.



## ii. Using [ROS wrapper](https://github.com/MIT-SPARK/Kimera-VIO-ROS)



We provide a ROS wrapper of Kimera-VIO that you can find at: https://github.com/MIT-SPARK/Kimera-VIO-ROS.



This library can be cloned into a catkin workspace and built alongside the ROS wrapper.



## iii. Evaluation and Debugging



For more information on tools for debugging and evaluating the pipeline, see [our documentation](/docs/kimera_vio_debug_evaluation.md)



## iv. Unit Testing



We use [gtest](https://github.com/google/googletest) for unit testing.

To run the unit tests: build the code, navigate inside the `build` folder and run `testKimeraVIO`:

```bash

cd build

./testKimeraVIO

```



A useful flag is `./testKimeraVIO --gtest_filter=foo` to only run the test you are interested in (regex is also valid).



Alternatively, you can run `rosrun kimera_vio run_gtest.py` from anywhere on your system if you've built Kimera-VIO through ROS and sourced the workspace containing Kimera-VIO. This script passes all arguments to `testKimeraVIO`, so you should feel free to use whatever flags you would normally use.



# 3. Parameters

Kimera-VIO accepts two independent sources of parameters:

- YAML files: contains parameters for Backend and Frontend.

- [gflags](https://gflags.github.io/gflags/) contains parameters for all the rest.



To get help on what each gflag parameter does, just run the executable with the `--help` flag: `./build/stereoVIOEuroc --help`. You should get a list of gflags similar to the ones [here](./docs/gflags_parameters.md).



  - Optionally, you can try the VIO using structural regularities, as in [our ICRA 2019 paper](https://ieeexplore.ieee.org/abstract/document/8794456), by specifying the option ```-r```: ```./stereoVIOEuroc.bash -p "PATH_TO_DATASET/V1_01_easy" -r```



OpenCV's 3D visualization also has some shortcuts for interaction: check [tips for usage](./docs/tips_usage.md)



Camera parameters can be described using the pinhole model or the omni model. The omni model is based on the OCamCalib toolbox described in [this paper](http://rpg.ifi.uzh.ch/docs/CCMVS2007_scaramuzza.pdf). A tutorial for generating the calibration can be found [here](https://sites.google.com/site/scarabotix/ocamcalib-toolbox).



The Omni camera model requires these additional parameters:



```

omni_affine

omni_distortion_center

```



The distortion polynomial is stored in the `distortion_coefficients` field. The matlab toolbox gives only 4 coefficients as output, however Kimera supports 5 coefficients. The second one can be set to zero. For example, if your output from OCamCalib is `[1, 2, 3, 4]` then you can set `distortion_coefficients` to `[1, 0, 2, 3, 4]` in the camera parameters file.



Inverse polynomial for projection is not required. In the omni camera case, the `intrinsics` field represents the intrinsics of a **ideal pinhole model** of the fisheye camera, which is primarily used when instantiating gtsam calibrations that currently are only implemented for pinhole cameras. Leaving it blank is sufficient as the code will generate a ideal model based on the image size. You may supply your own ideal pinhole intrinsics and they will be used instead. In the pinhole case, these values must be supplied consistently with the camera parameters (focal lengths and image center). 



# 4. Contribution guidelines



We strongly encourage you to submit issues, feedback and potential improvements.

We follow the branch, open PR, review, and merge workflow.



To contribute to this repo, ensure your commits pass the linter pre-commit checks.

To enable these checks you will need to [install linter](./docs/linter_install.md).

We also provide a `.clang-format` file with the style rules that the repo uses, so that you can use [`clang-format`](https://clang.llvm.org/docs/ClangFormat.html) to reformat your code.



Also, check [tips for development](./docs/tips_development.md) and our **[developer guide](./docs/developer_guide.md)**.



# 5. FAQ



### Issues

  If you have problems building or running the pipeline and/or issues with dependencies, you might find useful information in our [FAQ](./docs/faq.md) or in the issue tracker.



### How to interpret console output



```

I0512 21:05:55.136549 21233 Pipeline.cpp:449] Statistics

-----------                                  #	Log Hz	{avg     +- std    }	[min,max]

Data Provider [ms]                      	    0	

Display [ms]                            	  146	36.5421	{8.28082 +- 2.40370}	[3,213]

VioBackend [ms]                         	   73	19.4868	{15.2192 +- 9.75712}	[0,39]

VioFrontend Frame Rate [ms]             	  222	59.3276	{5.77027 +- 1.51571}	[3,12]

VioFrontend Keyframe Rate [ms]          	   73	19.6235	{31.4110 +- 7.29504}	[24,62]

VioFrontend [ms]                        	  295	77.9727	{12.1593 +- 10.7279}	[3,62]

Visualizer [ms]                         	   73	19.4639	{3.82192 +- 0.805234}	[2,7]

backend_input_queue Size [#]            	   73	18.3878	{1.00000 +- 0.00000}	[1,1]

data_provider_left_frame_queue Size (#) 	  663	165.202	{182.265 +- 14.5110}	[1,359]

data_provider_right_frame_queue Size (#)	  663	165.084	{182.029 +- 14.5150}	[1,359]

display_input_queue Size [#]            	  146	36.5428	{1.68493 +- 0.00000}	[1,12]

stereo_frontend_input_queue Size [#]    	  301	75.3519	{4.84718 +- 0.219043}	[1,5]

visualizer_backend_queue Size [#]       	   73	18.3208	{1.00000 +- 0.00000}	[1,1]

visualizer_frontend_queue Size [#]      	  295	73.9984	{4.21695 +- 1.24381}	[1,7]

```



- `#` number of samples taken.

- `Log Hz` average number of samples taken per second in Hz.

- `avg` average of the actual value logged. Same unit as the logged quantity.

- `std` standard deviation of the value logged.

- `[min,max]` minimum and maximum values that the logged value took.



There are two main things logged: the time it takes for the pipeline modules to run (i.e. `VioBackend`, `Visualizer` etc), and the size of the queues between pipeline modules (i.e. `backend_input_queue`).



For example:

```

VioBackend [ms]                         	   73	19.4868	{15.2192 +- 9.75712}	[0,39]

```



Shows that the Backend runtime got sampled `73` times, at a rate of `19.48`Hz (which accounts for both the time the Backend waits for input to consume and the time it takes to process it). That it takes `15.21`ms to consume its input with a standard deviation of `9.75`ms and that the least it took to run for one input was `0`ms and the most it took so far is `39`ms.



For the queues, for example:

```

stereo_frontend_input_queue Size [#]    	  301	75.3519	{4.84718 +- 0.219043}	[1,5]

```



Shows that the Frontend input queue got sampled `301` times, at a rate of `75.38`Hz. That it stores an average of `4.84` elements, with a standard deviation of `0.21` elements, and that the min size it had was `1` element, and the max size it stored was of `5` elements.



# 6. Chart



![vio_chart](./docs/media/kimeravio_chart.png)



![overall_chart](./docs/media/kimera_chart_23.jpeg)



# 7. BSD License



Kimera-VIO is open source under the BSD license, see the [LICENSE.BSD](LICENSE.BSD) file.