Multi-Modal Multi-Lidar-Inertial Odometry and Mapping Implementation
License: MIT License
multi-modal-loam's Introduction
MMLOAM : Robust Multi-Modal Multi-LiDAR-Inertial Odometry and Mapping for Indoor Environments
This is the repository for the code implementation, results, and dataset of the paper titled "Robust Multi-Modal Multi-LiDAR-Inertial Odometry and Mapping for Indoor Environments", which is currently under review for IROS 2023. Our team is currently organizing the code and we plan to make it open source on May 1st.
The current version of the paper can be accessed at here.
Introduction
We propose a tightly-coupled multi-modal multi-LiDAR-inertial SLAM system for surveying and mapping tasks. By taking advantage of both solid-state and spinnings LiDARs, and built-in inertial measurement units (IMU). First, we use spatial-temporal calibration modules to align the timestamp and calibrate extrinsic parameters between sensors. Then, we extract two groups of feature points including edge and plane points, from LiDAR data. Next, with pre-integrated IMU data, an undistortion module is applied to the LiDAR point cloud data. Finally, the undistorted point clouds are merged into one point cloud and processed with a sliding window based optimization module.
System Pipeline
- (left )Data collecting platform. Hardware platform used for data acquisition. The sensors used in this work are the Livox Horizon LiDAR, with its built-in IMU,
and the Velodyne VLP-16 LiDAR;
(Right) The pipeline of proposed multi-modal LiDAR-inertial odometry and mapping framework. The system starts with preprocessingmodule which takes the input from sensors and performs IMU pre-integration, calibrations, and un-distortions. The scan registration module extracts features and sent the features to a tightly coupled sliding window odometry. Finally, a pose graph is built to maintain global consistency
- Mapping result with the proposed system at a hall environment.
Thanks to the high resolution of solid-state LiDAR with a non-repetitive
scan pattern, the mapping result is able to show clear detail of object’s
surface.
Hardware and Mapping result in long corridor environment. Our proposed methods show robust performance in long corridor environments and survive in narrow spaces where 180°U-turn occur.
Qualitative comparison of map details in the office room dataset sequence. The color of the points represents the reflectivity provided by raw sensor data. The point size is 0.01, and transparency is set to 0.05. The middle two columns show the zoom-in view of the wall (top) and TV (bottom).
Mapping results with the proposed method outdoors: urban street (left) and forest environment (right)..
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