On Uncertainty Quantification for Convolutional Neural Network LiDAR Localization
On Uncertainty Quantification for Convolutional Neural Network LiDAR Localization
- New search for: Joerger, Mathieu
- New search for: Wang, Julian
- New search for: Hassani, Ali
- New search for: Joerger, Mathieu
- New search for: Wang, Julian
- New search for: Hassani, Ali
In this paper, we develop and evaluate a Convolutional Neural Network (CNN)-based Light Detection and Ranging (LiDAR) localization algorithm that includes uncertainty quantification for ground vehicle navigation. This paper builds upon prior research where we used a CNN to estimate a rover’s position and orientation (pose) using LiDAR point clouds (PCs). This paper presents a simplification of the LiDAR PC processing and describes a new approach for outputting a covariance matrix in addition to the rover pose estimates. Performance assessment is carried out in a structured, static lab environment using a LiDAR-equipped rover moving along a fixed, repeated trajectory.
Document information
On Uncertainty Quantification for Convolutional Neural Network LiDAR Localization
2022 IEEE Intelligent Vehicles Symposium (IV) ; 1789-1794
2022-06-05
1796967 byte
Conference paper
Electronic Resource
English
Similar titles
LiDAR and Camera-Based Convolutional Neural Network Detection for Autonomous Driving
| SAE Technical Papers | 2020