Robust localization using 3D NDT scan matching with experimentally determined uncertainty and road marker matching

By Naoki Akai, Luis Yoichi Morales Saiki, Eijiro Takeuchi, Yuki Yoshihara, Yoshiki Ninomiya. 2017

101 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. Kazuya Takeda
2. Ekim Yurtsever
3. Alexander Carballo
4. Jacob Lambert
5. Yoshiki Ninomiya
6. Naoki Akai
7. Luis Yoichi Morales Saiki
8. Yuki Yoshihara

In Environmental Engineering

1. Kazuya Takeda
2. Alexander Carballo
3. Ekim Yurtsever
4. Jacob Lambert

In Geology

1. Kazuya Takeda
2. Alexander Carballo
3. Ekim Yurtsever
4. Yoshiki Ninomiya
5. Eijiro Takeuchi
6. Jacob Lambert

Citations in the corpus

• A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “An improved version of 3D NDT matching was proposed in [97], and [114] augmented NDT with road marker matching.”
• Autonomous driving based on accurate localization using multilayer LiDAR and dead reckoning (2017, methodology): “We also proposed a localization method based on point cloud and road marker matching [7], where we employed Monte Carlo localization (MCL) [8] to fuse the estimated poses for the two maps and dead reckoning.”
• Driving Feature Extraction and Behavior Classification Using an Autoencoder to Reproduce the Velocity Styles of Experts (2018, unknown): “We used a 3D localization system based on a combination of scan matching using normal distance transform and dead reckoning [22].”