Fast LIDAR localization using multiresolution Gaussian mixture maps

By Ryan W. Wolcott, Ryan M. Eustice. 2015

188 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Environmental Engineering, Geology

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. Sertac Karaman
2. Fangchang Ma
3. Jason Mars
4. Hao Li
5. Daniela Rus
6. Shenhua Hou
7. Md E. Haque
8. Yao Zhou

In Environmental Engineering

1. Sertac Karaman
2. Jason Mars
3. Daniela Rus
4. Shenhua Hou
5. Md E. Haque
6. Yao Zhou
7. Lingjia Tang
8. Chang-Hong Hsu

In Geology

1. Sertac Karaman
2. Jason Mars
3. Daniela Rus
4. Md E. Haque
5. Yao Zhou
6. Lingjia Tang
7. Chang-Hong Hsu
8. Yunqi Zhang

Citations in the corpus

• Autonomous Vehicle Navigation in Rural Environments Without Detailed Prior Maps (2018, background): “[11]–[13], [22]–[24]), we assume no precise metric highresolution global map.”
• Robust and Precise Vehicle Localization Based on Multi-Sensor Fusion in Diverse City Scenes (2017, background): “Failure during harsh weather conditions and road construction still is an important issue of LiDARbased methods, although related works have shown good progress in solving these problems, for example, light rain [2] and snow [3].”
• The Architectural Implications of Autonomous Driving: Constraints and Acceleration (2018, methodology): “Therefore, prior map-based localization has beenwidely used to provide localization capability at centimeter-level precision [44, 53, 64, 78, 79, 83], where the surrounding view is transformed into feature descriptions to map the feature points stored in the prior map to identify the location of the”
• 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].”
• L3-Net: Towards Learning Based LiDAR Localization for Autonomous Driving (2019, methodology): “In the last decade, several methods have been proposed to achieve this goal with the help of 3D light detection and ranging (LiDAR) scanners [19, 20, 18, 36, 37, 17, 9, 32].”