Loop Closure SLAM Paper List - tkkim-robot/SLAM_Wiki GitHub Wiki

Proposal

SLAM papers and references for accurate mapping.

One way to create accurate map through Loop Closure is Graph SLAM. In order to solve the Loop Closure problem, recent papers have been actively introducing the concept of Graph Slam and have published papers that use more information to make more accurate maps. This page is for collection of list and review of these papers.

Lists

- BLAM(Berkeley Localization And Mapping)

BLAM is an open-source software package for LiDAR-based real-time 3D localization and mapping. BLAM is developed by Erik Nelson from the Berkeley AI Research Laboratory (BAIR)

• Repository https://github.com/erik-nelson/blam
• Video Examples https://www.youtube.com/watch?v=08GTGfNneCI

- HDL Graph SLAM

hdl_graph_slam is an open source ROS package for real-time 6DOF SLAM using a 3D LIDAR. It is based on 3D Graph SLAM with NDT scan matching-based odometry estimation and loop detection. It also supports several graph constraints, such as GPS, IMU acceleration (gravity vector), IMU orientation (magnetic sensor), and floor plane (detected in a point cloud). We have tested this package with Velodyne (HDL32e, VLP16) and RoboSense (16 channels) sensors in indoor and outdoor environments.

• Paper Kenji Koide, Jun Miura, and Emanuele Menegatti, A Portable 3D LIDAR-based System for Long-term and Wide-area People Behavior Measurement, Advanced Robotic Systems, 2019 link(https://www.researchgate.net/publication/331283709_A_portable_three-dimensional_LIDAR-based_system_for_long-term_and_wide-area_people_behavior_measurement).
• Repository https://github.com/koide3/hdl_graph_slam
• Video Examples https://drive.google.com/file/d/0B9f5zFkpn4soSG96Tkt4SFFTbms/view

- Google Cartographer

Cartographer ins a system that provides real-time simultaneous localization and mapping(SLAM) in 2D and 3D across multiple platforms and sensor configurations.

• Official Link https://opensource.google.com/projects/cartographer
• Docs
• Cartographer ROS integration https://google-cartographer-ros.readthedocs.io/en/latest/

- SuMa(Surfel-based Mapping for 3d Laser Range Data)

Mapping of 3d laser range data from a rotating laser range scanner, e.g., the Velodyne HDL-64E. For representing the map, we use surfels that enables fast rendering of the map for point-to-plane ICP and loop closure detection.

• Papers J. Behley, C. Stachniss. Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments, Proc. of Robotics: Science and Systems (RSS), 2018. link(http://www.roboticsproceedings.org/rss14/p16.pdf)
• Repository https://github.com/jbehley/SuMa
• Video Examples https://www.youtube.com/watch?v=-AEX203rXkE

- SegMap

SegMap is a map representation based on 3D segments allowing for robot localization, environment reconstruction, and semantics extraction. The SegMap code is open-source (BSD License) and has been tested under Ubuntu 14.04, 16.04 and ROS Indigo, Kinetic.

• Papers

R. Dubé, A. Cramariuc, D. Dugas, J. Nieto, R. Siegwart, and C. Cadena. "SegMap: 3D Segment Mapping using Data-Driven Descriptors." Robotics: Science and Systems (RSS), 2018. pdf - video

R. Dubé, MG. Gollub, H. Sommer, I. Gilitschenski, R. Siegwart, C. Cadena and , J. Nieto. "Incremental Segment-Based Localization in 3D Point Clouds." IEEE Robotics and Automation Letters, 2018. pdf

R. Dubé, D. Dugas, E. Stumm, J. Nieto, R. Siegwart, and C. Cadena. "SegMatch: Segment Based Place Recognition in 3D Point Clouds." IEEE International Conference on Robotics and Automation, 2017. pdf - video

• Repository https://github.com/ethz-asl/segmap
• Video Examples https://www.youtube.com/watch?v=JJhEkIA1xSE https://www.youtube.com/watch?v=iddCgYbgpjE https://www.youtube.com/watch?v=CMk4w4eRobg

Test Datasets

- KITTI Lidar Dataset (trusted test data)

Visual Odometry / SLAM Evaluation

http://www.cvlibs.net/datasets/kitti/eval_odometry.php