文件列表:

CMakeLists.txt (3171, 2023-02-06)
Licence (35149, 2023-02-06)
config (0, 2023-02-06)
config\deprecated (0, 2023-02-06)
config\deprecated\ISRM2.yaml (1740, 2023-02-06)
config\deprecated\ekf_param.yaml (7386, 2023-02-06)
config\deprecated\kitti_scdr.yaml (1367, 2023-02-06)
config\large_scale.yaml (1090, 2023-02-06)
config\large_scale_05.yaml (1091, 2023-02-06)
config\seq_00.yaml (1097, 2023-02-06)
config\seq_01.yaml (1214, 2023-02-06)
config\seq_02.yaml (1102, 2023-02-06)
config\seq_05.yaml (1147, 2023-02-06)
config\seq_07.yaml (1122, 2023-02-06)
config\viz_params.yaml (54, 2023-02-06)
config\your_own_env.yaml (836, 2023-02-06)
config\your_own_env_ouster.yaml (1153, 2023-02-06)
config\your_own_env_vel16.yaml (1172, 2023-02-06)
img (0, 2023-02-06)
img\00 (0, 2023-02-06)
img\00\00_erasor.png (134212, 2023-02-06)
img\00\00_octomap.png (120170, 2023-02-06)
img\00\00_ppl.png (95106, 2023-02-06)
img\00\00_raw.png (117503, 2023-02-06)
img\00\00_removert.png (127698, 2023-02-06)
img\01 (0, 2023-02-06)
img\01\01_erasor.png (302477, 2023-02-06)
img\01\01_octomap.png (214995, 2023-02-06)
img\01\01_ppl.png (199729, 2023-02-06)
img\01\01_raw.png (316031, 2023-02-06)
img\01\01_removert.png (330689, 2023-02-06)
img\02 (0, 2023-02-06)
img\02\02_erasor.png (203446, 2023-02-06)
img\02\02_octomap.png (166081, 2023-02-06)
img\02\02_ppl.png (142820, 2023-02-06)
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# :rainbow: ERASOR (RA-L'21 with ICRA Option) Official page of [*"ERASOR: Egocentric Ratio of Pseudo Occupancy-based Dynamic Object Removal for Static 3D Point Cloud Map Building"*](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9361109), which is accepted by RA-L with ICRA'21 option [[Video](https://www.youtube.com/watch?v=Nx27ZO8afm0)] [[Preprint Paper](https://arxiv.org/abs/2103.04316)]  We provide all contents including - [x] Source code of ERASOR - [x] All outputs of the State-of-the-arts - [x] Visualization - [x] Calculation code of Preservation Rate/Rejection Rate So enjoy our codes! :) Contact: Hyungtae Lim (shapelim`at`kaist`dot`ac`dot`kr) Advisor: Hyun Myung (hmyung`at`kaist`dot`ac`dot`kr) ## NEWS (Recent update: Oct., 2021) - An example of running ERASOR in your own env. is provided. - Please refer to please refer to `src/offline_map_updater/main_in_your_env.cpp` and `launch/run_erasor_in_your_env_vel16.launch`. The more details are [here](#ERASOR-in-the-Wild). --- ## Contents 0. [Test Env.](#Test-Env.) 0. [Requirements](#requirements) 0. [How to Run ERASOR](#How-to-Run-ERASOR) 0. [Calculate PR/RR](#Calculate-PR/RR) 0. [Benchmark](#benchmark) 0. [Visualization of All the State-of-the-arts](#Visualization-of-All-the-State-of-the-arts) 0. [ERASOR in the Wild](#ERASOR-in-the-Wild) 0. [Citation](#citation) ## Test Env. The code is tested successfully at * Linux 18.04 LTS * ROS Melodic ## Requirements ### ROS Setting - Install [ROS](http://torch.ch/docs/getting-started.html) on a machine. - Also, [jsk-visualization](https://github.com/jsk-ros-pkg/jsk_visualization) is required to visualize Scan Ratio Test (SRT) status. ```bash sudo apt-get install ros-melodic-jsk-recognition sudo apt-get install ros-melodic-jsk-common-msgs sudo apt-get install ros-melodic-jsk-rviz-plugins ``` #### Build Our Package - Thereafter, compile this package. We use [catkin tools](https://catkin-tools.readthedocs.io/en/latest/), ```bash mkdir -p ~/catkin_ws/src cd ~/catkin_ws/src git clone https://github.com/LimHyungTae/ERASOR.git cd .. && catkin build erasor ``` ### Python Setting - Our metric calculation for PR/RR code is implemented by python2.7 - To run the python code, following pakages are necessary: [pypcd](https://github.com/dimatura/pypcd), [tqdm](https://github.com/tqdm/tqdm), [scikit-learn](https://scikit-learn.org/stable/), and [tabulate](https://pyneng.readthedocs.io/en/latest/book/12_useful_modules/tabulate.html) ```bash pip install pypcd pip install tqdm pip install scikit-learn pip install tabulate ``` ### Prepared dataset - Download the preprocessed KITTI data encoded into rosbag. - The downloading process might take five minutes or so. All rosbags requires total 2.3G of storage space ```bash wget https://urserver.kaist.ac.kr/publicdata/erasor/rosbag/00_4390_to_4530_w_interval_2_node.bag wget https://urserver.kaist.ac.kr/publicdata/erasor/rosbag/01_150_to_250_w_interval_1_node.bag wget https://urserver.kaist.ac.kr/publicdata/erasor/rosbag/02_860_to_950_w_interval_2_node.bag wget https://urserver.kaist.ac.kr/publicdata/erasor/rosbag/05_2350_to_2670_w_interval_2_node.bag wget https://urserver.kaist.ac.kr/publicdata/erasor/rosbag/07_630_to_820_w_interval_2_node.bag ``` #### Description of Preprocessed Rosbag Files - Please note that the rosbag consists of `node`. Refer to `msg/node.msg`. - Note that each label of the point is assigned in `intensity` for the sake of convenience. - And we set the following classes are dynamic classes: ``` # 252: "moving-car" # 253: "moving-bicyclist" # 254: "moving-person" # 255: "moving-motorcyclist" # 256: "moving-on-rails" # 257: "moving-bus" # 258: "moving-truck" # 259: "moving-other-vehicle" ``` - Please refer to `std::vector DYNAMIC_CLASSES` in our code :). ## How to Run ERASOR We will explain how to run our code on seq 05 of the KITTI dataset as an example. **Step 1. Build naive map**  * Set the following parameters in `launch/mapgen.launch`. * `target_rosbag`: The name of target rosbag, e.g. `05_2350_to_2670_w_interval_2_node.bag` * `save_path`: The path where the naively accumulated map is saved. * Launch mapgen.launch and play corresponding rosbag on the other bash as follows: ```bash roscore # (Optional) roslaunch erasor mapgen.launch rosbag play 05_2350_to_2670_w_interval_2_node.bag ``` * Then, dense map and voxelized map are auto-saved at the `save path`. Note that the dense map is used for evaluation to fill corresponding labels. The voxelized map will be an input of step 2 as a naively accumulated map. **Step 2. Run ERASOR**  * Set the following parameters in `config/seq_05.yaml`. * `initial_map_path`: The path of naively accumulated map * `save_path`: The path where the filtered static map is saved. * Run the following command for each bash. ```bash roscore # (Optional) roslaunch erasor run_erasor.launch target_seq:="05" rosbag play 05_2350_to_2670_w_interval_2_node.bag ``` **News (22.03.01):** The submap module is employed to speed up when extracing map VOI. Plase check the below rosparams in `run_erasor.launch`: ``` true 160.0 ``` Note that appropriate `submap_size` is > 2 * max_range. * **IMPORTANT:** After finishing running ERASOR, run the following command to save the static map as a pcd file on another bash. * "0.2" denotes voxelization size. ```bash rostopic pub /saveflag std_msgs/Float32 "data: 0.2" ``` * Then, you can see the printed command as follows:  * The results will be saved under the `save_path` folder, i.e. `$save_path$/05_result.pcd`. ## Calculate PR/RR You can check our results directly. * First, download all pcd materials. ```bash wget https://urserver.kaist.ac.kr/publicdata/erasor/erasor_paper_pcds.zip unzip erasor_paper_pcds.zip ``` Then, run the analysis code as follows: ```bash python analysis.py --gt $GT_PCD_PATH$ --est $EST_PCD_PATH$ ``` E.g, ```bash python analysis.py --gt /home/shapelim/erasor_paper_pcds/gt/05_voxel_0_2.pcd --est /home/shapelim/erasor_paper_pcds/estimate/05_ERASOR.pcd ``` **NOTE**: For estimating PR/RR, more dense pcd file, which is generated in the `mapgen.launch` procedure, is better to estimate PR/RR precisely. ## Benchmark - Error metrics are a little bit different from those in the paper: | Seq. | PR [%] | RR [%] | |-----------------------------|:-----:|:-----:| | 00 | 91.72 | 97.00 | | 01 | 91.93 | 94.63 | | 02 | 81.08 | 99.11 | | 05 | 86.*** | 97.88 | | 07 | 92.00 | ***.33 | - But we provide all pcd files! Don't worry. See [Visualization of All the State-of-the-arts](#Visualization-of-All-the-State-of-the-arts) Section. ## Visualization of All the State-of-the-arts * First, download all pcd materials. ```bash wget https://urserver.kaist.ac.kr/publicdata/erasor/erasor_paper_pcds.zip unzip erasor_paper_pcds.zip ``` * Set parameters in `config/viz_params.yaml` correctly * `abs_dir`: The absolute directory of pcd directory * `seq`: Target sequence (00, 01, 02, 05, or 07) * After setting the parameters, launch following command: ```bash roslaunch erasor compare_results.launch ``` * Then you can inspect all pcd results that are already parsed into static points and dynamic points. * All examples are here: * [Sequence 00: 4,390~4,530](img/00) * [Sequence 01: 150~250](img/01) * [Sequence 02: 860~950](img/02) * [Sequence 05: 2,350~2,670](img/05) * [Sequence 07: 630~820](img/07) ## ERASOR in the Wild ### In your own dataset To check generalization of ERASOR, we tested ERASOR in more crowded environments. In that experiment, Velodyne Puck 16 was employed, and poses are estimated by [LIO-SAM](https://github.com/TixiaoShan/LIO-SAM). Satellite map | Pcd map by LIO-SAM :-------------------------:|:-------------------------:  |  When **running ERASOR in your own environments**, please refer to `src/offline_map_updater/main_in_your_env.cpp` file and `launch/run_erasor_in_your_env_vel16.launch`. You can learn how to set experimental setting by repeating our pre-set configurations. Please follow our instructions. * First, download pre-set dataset. ``` wget https://urserver.kaist.ac.kr/publicdata/erasor/bongeunsa_dataset.zip unzip bongeunsa_dataset.zip ``` * Modify `data_dir`, `MapUpdater/initial_map_path`, and `MapUpdater/save_path` in `config/your_own_env_vel16.yaml` to be right directory for your machine, where `data_dir` should consist of following components as follows: ``` `data_dir` _____pcds |___000000.pcd |___000001.pcd |___000002.pcd |... _____dense_global_map.pcd _____poses_lidar2body.csv _____... ``` * Next, launch `launch/run_erasor_in_your_env_vel16.launch` as follows: ``` roslaunch erasor run_erasor_in_your_env_vel16.launch ``` ### Results   ### Note: Setting appropriate parameters * As shown in `config`, depending on your own sensor configuration, parameters must be changed. In particular, `min_h` and `max_h`, and `th_bin_max_h` should be changed (note that `min_h` and `max_h`, and `th_bin_max_h` is w.r.t. your body frame of a query pcd file.) * If you use a low-channel LiDAR sensor such as Velodyne Puck-16, `max_r` and `num_rings` must be set as smaller values like `config/your_own_env_vel16.yaml` to guarantee the estimated normal vector for each bin is considered to be orthogonal to the ground. * If too many points are considered as ground points for each bin, then reduce the value of `gf_dist_thr`. ## Citation If you use our code or method in your work, please consider citing the following: @article{lim2021erasor, title={ERASOR: Egocentric Ratio of Pseudo Occupancy-Based Dynamic Object Removal for Static 3D Point Cloud Map Building}, author={Lim, Hyungtae and Hwang, Sungwon and Myung, Hyun}, journal={IEEE Robotics and Automation Letters}, volume={6}, number={2}, pages={2272--2279}, year={2021}, publisher={IEEE} }

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