Low-drift and real-time lidar odometry and mapping

By Ji Zhang, Sanjiv Singh. Auton. Robots, 2017

760 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology

View Paper: https://www.semanticscholar.org/paper/67e4d3933d35544023c32dcc06bf81206a35d16f

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Citations in the corpus

A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping (2020, methodology): “The work in [57] showed a particle ﬁlter applied to 2D SLAM able to compute a highly accurate proposal distribution based on a likelihood model.”
MulRan: Multimodal Range Dataset for Urban Place Recognition (2020, methodology): “The robustness to illumination change and measurement precision has allowed many LiDAR-based methods to outperform vision-based methods [13].”
SuMa++: Efficient LiDAR-based Semantic SLAM (2019, methodology): “IMLS-SLAM [8] and LOAM [41] are state-ofthe-art LiDAR-based SLAM approaches.”
RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation (2018, methodology): “When comparing LOAM ‐ RTAB and LOAM against each other, either the open ‐ source version used (with default parameters for this kind of Velodyne) seems to not reproduce the original algorithm perfectly, or some parameters tuning is required, as we expected that results would be more similar.”
LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping (2020, methodology): “For the sake of brevity, the detailed procedures for finding these correspondences are omitted here, but are described thoroughly in [1].”
LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain (2018, methodology): “The feature extraction process is similar to the method used in [20].”
IMLS-SLAM: Scan-to-Model Matching Based on 3D Data (2018, methodology): “For comparison, as explained in [7], LOAM runs at 1 s per scan on the KITTI dataset.”
LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping (2021, methodology): “LOAM [9] and LeGOLOAM [10] are loosely-coupled systems, as the IMU measurements are not used in the optimization step.”
Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments (2018, methodology): “mapping systems mainly accomplish the estimation relying on feature-based solutions [34, 35], reduced map representations [14, 13], voxel grid-based methods [16], or point sub-sampling [30], which all effectively reduce the data used for alignment.”