All Projects → SMRT-AIST → Interactive_slam

SMRT-AIST / Interactive_slam

Licence: gpl-3.0
Interactive Map Correction for 3D Graph SLAM

Projects that are alternatives of or similar to Interactive slam

Awesome Robotic Tooling
Tooling for professional robotic development in C++ and Python with a touch of ROS, autonomous driving and aerospace.
Stars: ✭ 1,876 (+404.3%)
Mutual labels:  ros, slam, point-cloud, lidar
Hdl graph slam
3D LIDAR-based Graph SLAM
Stars: ✭ 945 (+154.03%)
Mutual labels:  ros, slam, point-cloud, lidar
awesome-lidar
😎 Awesome LIDAR list. The list includes LIDAR manufacturers, datasets, point cloud-processing algorithms, point cloud frameworks and simulators.
Stars: ✭ 217 (-41.67%)
Mutual labels:  point-cloud, lidar, slam
urban road filter
Real-time LIDAR-based Urban Road and Sidewalk detection for Autonomous Vehicles 🚗
Stars: ✭ 134 (-63.98%)
Mutual labels:  point-cloud, ros, lidar
Lidar camera calibration
ROS package to find a rigid-body transformation between a LiDAR and a camera for "LiDAR-Camera Calibration using 3D-3D Point correspondences"
Stars: ✭ 734 (+97.31%)
Mutual labels:  ros, point-cloud, lidar
Awesome Visual Slam
📚 The list of vision-based SLAM / Visual Odometry open source, blogs, and papers
Stars: ✭ 1,336 (+259.14%)
Mutual labels:  ros, slam, point-cloud
Depth clustering
🚕 Fast and robust clustering of point clouds generated with a Velodyne sensor.
Stars: ✭ 657 (+76.61%)
Mutual labels:  ros, point-cloud, lidar
Loam velodyne
Laser Odometry and Mapping (Loam) is a realtime method for state estimation and mapping using a 3D lidar.
Stars: ✭ 1,135 (+205.11%)
Mutual labels:  ros, slam, lidar
Lego Loam
LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain
Stars: ✭ 1,138 (+205.91%)
Mutual labels:  ros, slam, lidar
Lidar camera calibration
Light-weight camera LiDAR calibration package for ROS using OpenCV and PCL (PnP + LM optimization)
Stars: ✭ 133 (-64.25%)
Mutual labels:  ros, point-cloud, lidar
direct lidar odometry
Direct LiDAR Odometry: Fast Localization with Dense Point Clouds
Stars: ✭ 202 (-45.7%)
Mutual labels:  ros, lidar, slam
BtcDet
Behind the Curtain: Learning Occluded Shapes for 3D Object Detection
Stars: ✭ 104 (-72.04%)
Mutual labels:  point-cloud, lidar
UrbanLoco
UrbanLoco: A Full Sensor Suite Dataset for Mapping and Localization in Urban Scenes
Stars: ✭ 147 (-60.48%)
Mutual labels:  lidar, slam
3d cnn tensorflow
KITTI data processing and 3D CNN for Vehicle Detection
Stars: ✭ 266 (-28.49%)
Mutual labels:  point-cloud, lidar
ROS
ROS机器人操作系统 学习(写于2020年夏)
Stars: ✭ 102 (-72.58%)
Mutual labels:  ros, slam
lidar body tracking
ROS Catkin package to track people using octree and cluster extraction
Stars: ✭ 68 (-81.72%)
Mutual labels:  ros, lidar
Zed Ros Wrapper
ROS wrapper for the ZED SDK
Stars: ✭ 284 (-23.66%)
Mutual labels:  ros, slam
Se2lam
(ICRA 2019) Visual-Odometric On-SE(2) Localization and Mapping
Stars: ✭ 285 (-23.39%)
Mutual labels:  ros, slam
2019-UGRP-DPoom
2019 DGIST DPoom project under UGRP : SBC and RGB-D camera based full autonomous driving system for mobile robot with indoor SLAM
Stars: ✭ 35 (-90.59%)
Mutual labels:  ros, slam
Camlasercalibratool
Extrinsic Calibration of a Camera and 2d Laser
Stars: ✭ 277 (-25.54%)
Mutual labels:  ros, slam

interactive_slam

interactive_slam is an open source 3D LIDAR-based mapping framework. In contrast to existing automatic SLAM packages, we aim to develop a semi-automatic framework which allows the user to interactively and intuitively correct mapping failures (e.g., corrupted odometry, wrong loop detection, distorted map, etc) with minimal human effort. This framework provides several map correction features:

  • [Manual & Automatic] Loop closing
  • [Manual] Plane-based map correction
  • [Manual] Multiple map merging
  • [Automatic] Pose edge refinement

Screenshot_20191016_182424 [video]

This package is built on top of the ROS ecosystem. You can start building a map with a pose graph constructed by hdl_graph_slam or a customized LeGO-LOAM, or odometry data generated by any ROS package.

This package has been tested on Ubuntu 18.04 & ROS melodic or later.

Codacy Badge Build Status

Installation

interactive_slam depends on the following libraries:

# for ROS melodic
sudo apt-get install libglm-dev libglfw3-dev
sudo apt-get install ros-melodic-geodesy ros-melodic-pcl-ros ros-melodic-nmea-msgs ros-melodic-libg2o
cd ~/catkin_ws/src
git clone https://github.com/koide3/ndt_omp
git clone https://github.com/koide3/hdl_graph_slam
git clone https://github.com/koide3/odometry_saver
git clone https://github.com/SMRT-AIST/fast_gicp --recursive
git clone https://github.com/SMRT-AIST/interactive_slam --recursive

cd ~/catkin_ws
catkin_make -DCMAKE_BUILD_TYPE=Release

ROS Kinetic users
This package cannot be built using gcc and ld on Ubuntu 16. If you are on Ubuntu 16 and ROS kinetic, try the LLVM toolchain. Note: we recommend to use this package on melodic because we do only build-test but not run-test on kinetic.

sudo apt install clang-6.0 lld-6.0
sudo update-alternatives --install /usr/bin/ld ld /usr/bin/ld.lld-6.0 50
sudo update-alternatives --install /usr/bin/clang clang /usr/bin/clang-6.0 50
sudo update-alternatives --install /usr/bin/clang++ clang++ /usr/bin/clang++-6.0 50
cd ~/catkin_ws && rm -rf build devel    # be aware of that this command removes build and devel directories
CC=clang CXX=clang++ catkin_make -DCMAKE_BUILD_TYPE=Release

Examples

Example1 - Basic usage with hdl_graph_slam

In this example, we edit a map (pose graph) constructed by hdl_graph_slam. See more. Screenshot_20191016_175924 png

Example2 - Generating odometry with external ROS package

In this example, we create a map with odometry data generated from a rosbag file with LeGO-LOAM. See more.

Example3 - Plane-based map correction & Map merging

In this example, we correct a largely bent map with plane constraints and merge it with another map. See more.

Screenshot_20191016_182955 png

Graph/Odometry file format

You can feed graph/odometry files generated by your program to interactive_slam. See more

FAQ

FAQ

License

interactive_slam is released under GPLv3 license.

Related packages

Papers

Kenji Koide, Jun Miura, Masashi Yokozuka, Shuji Oishi, and Atsuhiko Banno, Interactive 3D Graph SLAM for Map Correction, IEEE Robotics and Automation Letters (RA-L), 2020 DOI

Contact

発展版機能について

Kenji Koide, [email protected], https://staff.aist.go.jp/k.koide

Mobile Robotics Research Team
National Institute of Advanced Industrial Science and Technology (AIST), Japan [URL]

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].