LOAM: Lidar Odometry and Mapping in Real-time

By Ji Zhang, Sanjiv Singh. Robotics: Science and Systems Conference, 2014

3,191 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/4d0fc815dced9db3954ed21060e9894e1555ee3d

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Citations in the corpus

Aggressive Deep Driving: Combining Convolutional Neural Networks and Model Predictive Control (2017, background): “Many SLAM approaches that use cameras [8, 9], LIDAR [10], or other sensor combinations [11] can provide accurate position.”
Incremental and Enhanced Scanline-Based Segmentation Method for Surface Reconstruction of Sparse LiDAR Data (2016, methodology): “There are many existing methods, such as ICP [29], Generalized-ICP [30], LOAM [12] and semantic alignment [31] to calculate the transformation matrix.”
DeepVCP: An End-to-End Deep Neural Network for Point Cloud Registration (2019, background): “It is a well-known problem and plays an essential role in many applications, such as Li-DAR SLAM [40, 6, 14, 20], 3D reconstruction and mapping ∗ Author to whom correspondence should be addressed [29, 8, 35, 7], positioning and localization [38, 16, 33, 18], object pose estimation [34] and so on.”
Aggressive Deep Driving: Model Predictive Control with a CNN Cost Model (2017, methodology): “There are many SLAM approaches that use cameras [8, 9], LIDAR [10], or other sensor combinations [11] to provide accurate position.”
Voxelized GICP for Fast and Accurate 3D Point Cloud Registration (2020, background): “Many features have been proposed for point cloud registration, such as basic plane and edge features [10], [4], Fast Point Feature Histogram (FPFH) [11], and Signature of Histograms of OrienTations (SHOT) [12].”
Online Learned Continual Compression with Stacked Quantization Module (2019, methodology): “We note that reconstruction quality has been previously linked to performance on downstream tasks such as SLAM Zhang & Singh (2014).”
Tightly Coupled 3D Lidar Inertial Odometry and Mapping (2019, methodology): “To ensure efficient estimation, many works on lidar mapping, like [7], [1] and [13], separate the task into two parts, the odometry and the mapping.”
SegMatch: Segment based place recognition in 3D point clouds (2016, background): “Zhang and Singh [12] are able to estimate odometry in real-time using range data.”
A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors (2019, methodology): “Impressive approaches over the last decades include visual-based methods [1]–[5], LiDAR-based methods [12], RGB-D based methods [13], and event-based methods [14].”
Self-Supervised Sparse-to-Dense: Self-Supervised Depth Completion from LiDAR and Monocular Camera (2018, background): “php?benchmark=depth_completion Depth sensing is fundamental in a variety of robotic tasks, including obstacle avoidance, 3D mapping [1, 2], and localization [3].”
FAST-LIO2: Fast Direct LiDAR-Inertial Odometry (2021, background): “Existing works on 3D LiDAR SLAM typically inherit the LOAM structure proposed in [23].”
LINS: A Lidar-Inertial State Estimator for Robust and Efficient Navigation (2019, methodology): “IMU-aided LOAM [8] took the orientation and translation calculated by the IMU as priors for optimization.”
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, methodology): “Zhang & Singh (2014) split the SLAM problem into LiDAR-based odometry at high frequency with low fidelity and LiDAR-mapping at low frequency illustrated in Figure 32.”
The Multivehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception (2018, methodology): “For each sequence with lidar measurements, we run the Lidar Odometry and Mapping (LOAM) algorithm [29] to generate dense 3D local maps, which are projected into each DAVIS camera to generate dense depth images at 20 Hz, and to provide 3D pose for the handheld sequences.”
LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain (2018, methodology): “The laptop CPU was selected to match the computing hardware used in [19] and [20].”
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.”