# Scene-Aware Error Modeling of LiDAR/Visual Odometry for Fusion-based Vehicle Localization

arXiv: https://arxiv.org/abs/2003.13109 · HTML: https://mlsanity.com/paper/2003.13109/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2020.

## Built on by (most cited first, 1 of 1)

- [CbwLoss: Constrained Bidirectional Weighted Loss for Self-supervised Learning of Depth and Pose](https://mlsanity.com/paper/2212.05845/) (2022)

## Abstract (arXiv)

Localization is an essential technique in mobile robotics. In a complex environment, it is necessary to fuse different localization modules to obtain more robust results, in which the error model plays a paramount role. However, exteroceptive sensor-based odometries (ESOs), such as LiDAR/visual odometry, often deliver results with scene-related error, which is difficult to model accurately. To address this problem, this research designs a scene-aware error model for ESO, based on which a multimodal localization fusion framework is developed. In addition, an end-to-end learning method is proposed to train this error model using sparse global poses such as GPS/IMU results. The proposed method is realized for error modeling of LiDAR/visual odometry, and the results are fused with dead reckoning to examine the performance of vehicle localization. Experiments are conducted using both simulation and real-world data of experienced and unexperienced environments, and the experimental results demonstrate that with the learned scene-aware error models, vehicle localization accuracy can be largely improved and shows adaptiveness in unexperienced scenes.