Optimized SC-F-LOAM: Optimized Fast LiDAR Odometry and Mapping Using Scan Context

by   Lizhou Liao, et al.

LiDAR odometry can achieve accurate vehicle pose estimation for short driving range or in small-scale environments, but for long driving range or in large-scale environments, the accuracy deteriorates as a result of cumulative estimation errors. This drawback necessitates the inclusion of loop closure detection in a SLAM framework to suppress the adverse effects of cumulative errors. To improve the accuracy of pose estimation, we propose a new LiDAR-based SLAM method which uses F-LOAM as LiDAR odometry, Scan Context for loop closure detection, and GTSAM for global optimization. In our approach, an adaptive distance threshold (instead of a fixed threshold) is employed for loop closure detection, which achieves more accurate loop closure detection results. Besides, a feature-based matching method is used in our approach to compute vehicle pose transformations between loop closure point cloud pairs, instead of using the raw point cloud obtained by the LiDAR sensor, which significantly reduces the computation time. The KITTI dataset and a UGV platform are used for verifications of our method, and the experimental results demonstrate that the proposed method outperforms typical LiDAR odometry/SLAM methods in the literature. Our code is made publicly available for the benefit of the community.


page 1

page 3

page 5


CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure

Multi-beam LiDAR sensors are increasingly used in robotics, particularly...

SA-LOAM: Semantic-aided LiDAR SLAM with Loop Closure

LiDAR-based SLAM system is admittedly more accurate and stable than othe...

3D-SeqMOS: A Novel Sequential 3D Moving Object Segmentation in Autonomous Driving

For the SLAM system in robotics and autonomous driving, the accuracy of ...

Efficient WiFi LiDAR SLAM for Autonomous Robots in Large Environments

Autonomous robots operating in indoor and GPS denied environments can us...

6-DOF Feature based LIDAR SLAM using ORB Features from Rasterized Images of 3D LIDAR Point Cloud

An accurate and computationally efficient SLAM algorithm is vital for mo...

PaGO-LOAM: Robust Ground-Optimized LiDAR Odometry

Numerous researchers have conducted studies to achieve fast and robust g...

Fast and Accurate Deep Loop Closing and Relocalization for Reliable LiDAR SLAM

Loop closing and relocalization are crucial techniques to establish reli...

Please sign up or login with your details

Forgot password? Click here to reset