# DPC-Net: Deep Pose Correction for Visual Localization

arXiv: https://arxiv.org/abs/1709.03128 · HTML: https://mlsanity.com/paper/1709.03128/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2017.

## Built on by (most cited first, 5 of 5)

- [Information-Theoretic Odometry Learning](https://mlsanity.com/paper/2203.05724/) (2022)
- [HDPV-SLAM: Hybrid Depth-augmented Panoramic Visual SLAM for Mobile Mapping System with Tilted LiDAR and Panoramic Visual Camera](https://mlsanity.com/paper/2301.11823/) (2023)
- [Deep Inertial Odometry with Accurate IMU Preintegration](https://mlsanity.com/paper/2101.07061/) (2021)
- [RAM-VO: Less is more in Visual Odometry](https://mlsanity.com/paper/2107.02974/) (2021)
- [Online Learning of Wheel Odometry Correction for Mobile Robots with Attention-based Neural Network](https://mlsanity.com/paper/2303.11725/) (2023)

## Abstract (arXiv)

We present a novel method to fuse the power of deep networks with the computational efficiency of geometric and probabilistic localization algorithms. In contrast to other methods that completely replace a classical visual estimator with a deep network, we propose an approach that uses a convolutional neural network to learn difficult-to-model corrections to the estimator from ground-truth training data. To this end, we derive a novel loss function for learning SE(3) corrections based on a matrix Lie groups approach, with a natural formulation for balancing translation and rotation errors. We use this loss to train a Deep Pose Correction network (DPC-Net) that predicts corrections for a particular estimator, sensor and environment. Using the KITTI odometry dataset, we demonstrate significant improvements to the accuracy of a computationally-efficient sparse stereo visual odometry pipeline, that render it as accurate as a modern computationally-intensive dense estimator. Further, we show how DPC-Net can be used to mitigate the effect of poorly calibrated lens distortion parameters.