# ViPR: Visual-Odometry-aided Pose Regression for 6DoF Camera Localization

arXiv: https://arxiv.org/abs/1912.08263 · HTML: https://mlsanity.com/paper/1912.08263/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2019.

## Built on by (most cited first, 11 of 11)

- [Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments](https://mlsanity.com/paper/2304.07250/) (2023)
- [PixSelect: Less but Reliable Pixels for Accurate and Efficient Localization](https://mlsanity.com/paper/2206.03775/) (2022)
- [Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression](https://mlsanity.com/paper/2208.00919/) (2022)
- [The Need for Inherently Privacy-Preserving Vision in Trustworthy Autonomous Systems](https://mlsanity.com/paper/2303.16408/) (2023)
- [Global Localization: Utilizing Relative Spatio-Temporal Geometric Constraints from Adjacent and Distant Cameras](https://mlsanity.com/paper/2312.00500/) (2023)
- [Implicit Learning of Scene Geometry from Poses for Global Localization](https://mlsanity.com/paper/2312.02029/) (2023)
- [NeuroLoc: Encoding Navigation Cells for 6-DOF Camera Localization](https://mlsanity.com/paper/2505.01113/) (2025)
- [From Single Scan to Sequential Consistency: A New Paradigm for LIDAR Relocalization](https://mlsanity.com/paper/2602.03198/) (2026)
- [Exploitation of Hidden Context in Dynamic Movement Forecasting: A Neural Network Journey from Recurrent to Graph Neural Networks and General Purpose Transformers](https://mlsanity.com/paper/2605.14855/) (2026)
- [PDRNN: Modular Data-driven Pedestrian Dead Reckoning on Loosely Coupled Radio- and Inertial-Signalstreams](https://mlsanity.com/paper/2605.15252/) (2026)
- [Temporal-Aware Fusion for Robust Outdoor LiDAR Localization](https://mlsanity.com/paper/2609.36432/) (2026)

## Abstract (arXiv)

Visual Odometry (VO) accumulates a positional drift in long-term robot navigation tasks. Although Convolutional Neural Networks (CNNs) improve VO in various aspects, VO still suffers from moving obstacles, discontinuous observation of features, and poor textures or visual information. While recent approaches estimate a 6DoF pose either directly from (a series of) images or by merging depth maps with optical flow (OF), research that combines absolute pose regression with OF is limited. We propose ViPR, a novel modular architecture for long-term 6DoF VO that leverages temporal information and synergies between absolute pose estimates (from PoseNet-like modules) and relative pose estimates (from FlowNet-based modules) by combining both through recurrent layers. Experiments on known datasets and on our own Industry dataset show that our modular design outperforms state of the art in long-term navigation tasks.