Loam livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV

By Jiarong Lin, Fu Zhang. IEEE International Conference on Robotics and Automation, 2019

362 citations recorded by the source corpus.

LiDAR odometry and mapping (LOAM) has been playing an important role in autonomous vehicles, due to its ability to simultaneously localize the robot’s pose and build high-precision, high-resolution maps of the surrounding environment. This enables autonomous navigation and safe path planning of autonomous vehicles. In this paper, we present a robust, real-time LOAM algorithm for LiDARs with small FoV and irregular samplings. By taking effort on both frontend and back-end, we address several fundamental challenges arising from such LiDARs, and achieve better performance in both precision and efficiency compared to existing baselines. To share our findings and to make contributions to the community, we open source our codes on Github1.

Subfields: Aerospace Engineering, Environmental Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/7300a6df0b659977fa08630d2eded70718767356

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Fu Zhang
2. Wei Xu
3. Jiarong Lin
4. Dongjiao He
5. Wei Xu
6. Yixi Cai

In Environmental Engineering

1. Wei Xu
2. Fu Zhang
3. Jiarong Lin

In Geology

1. Fu Zhang
2. Wei Xu
3. Jiarong Lin
4. Dongjiao He
5. Wei Xu
6. Yixi Cai

Citations in the corpus

• R3LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package (2021, background): “5˝ while the FoV of the LiDAR is 70.4˝ ˆ 77.2˝.”
• FAST-LIO2: Fast Direct LiDAR-Inertial Odometry (2021, background): “This situation is considerably worsened if the LiDAR Field of View (FoV) is small, a typical phenomenon of emerging solid-state LiDARs [16].”
• FAST-LIO: A Fast, Robust LiDAR-Inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter (2020, methodology): “While these methods work well for structured environments and LiDARs of large FoV, they are very vulnerable to featureless environments or small FoV LiDARs [11].”