# Attention-based Vehicle Self-Localization with HD Feature Maps

arXiv: https://arxiv.org/abs/2107.07787 · HTML: https://mlsanity.com/paper/2107.07787/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 16 Jul 2021.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [AtLoc: Attention Guided Camera Localization](https://mlsanity.com/paper/1909.03557/) (2019)
- [VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry](https://mlsanity.com/paper/1804.08366/) (2018)
- [A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence](https://mlsanity.com/paper/2006.12567/) (2020)
- [Traffic Control Gesture Recognition for Autonomous Vehicles](https://mlsanity.com/paper/2007.16072/) (2020)
- [Robust Optimization for Deep Regression](https://mlsanity.com/paper/1505.06606/) (2015)
- [Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets](https://mlsanity.com/paper/1901.02000/) (2019)
- [DeepLocalization: Landmark-based Self-Localization with Deep Neural Networks](https://mlsanity.com/paper/1904.09007/) (2019)
- [Deep Object Tracking on Dynamic Occupancy Grid Maps Using RNNs](https://mlsanity.com/paper/1805.08986/) (2018)
- [Extended Existence Probability Using Digital Maps for Object Verification](https://mlsanity.com/paper/2003.10316/) (2020)

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

- [A Survey on Visual Map Localization Using LiDARs and Cameras](https://mlsanity.com/paper/2208.03376/) (2022)

## Abstract (arXiv)

We present a vehicle self-localization method using point-based deep neural networks. Our approach processes measurements and point features, i.e. landmarks, from a high-definition digital map to infer the vehicle's pose. To learn the best association and incorporate local information between the point sets, we propose an attention mechanism that matches the measurements to the corresponding landmarks. Finally, we use this representation for the point-cloud registration and the subsequent pose regression task. Furthermore, we introduce a training simulation framework that artificially generates measurements and landmarks to facilitate the deployment process and reduce the cost of creating extensive datasets from real-world data. We evaluate our method on our dataset, as well as an adapted version of the Kitti odometry dataset, where we achieve superior performance compared to related approaches; and additionally show dominant generalization capabilities.