# Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots

arXiv: https://arxiv.org/abs/1708.06822 · HTML: https://mlsanity.com/paper/1708.06822/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2017.

## Built on by (most cited first, 10 of 10)

- [Bimodal Camera Pose Prediction for Endoscopy](https://mlsanity.com/paper/2204.04968/) (2022)
- [DeepAVO: Efficient Pose Refining with Feature Distilling for Deep Visual Odometry](https://mlsanity.com/paper/2105.09899/) (2021)
- [Reachability Analysis of Convolutional Neural Networks](https://mlsanity.com/paper/2106.12074/) (2021)
- [Learning-Based Depth and Pose Estimation for Monocular Endoscope with Loss Generalization](https://mlsanity.com/paper/2107.13263/) (2021)
- [BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo Surgical Image Matching](https://mlsanity.com/paper/2205.03133/) (2022)
- [Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery](https://mlsanity.com/paper/2106.07136/) (2021)
- [A high-precision self-supervised monocular visual odometry in foggy weather based on robust cycled generative adversarial networks and multi-task learning aided depth estimation](https://mlsanity.com/paper/2203.04812/) (2022)
- [Distance Metric-Based Learning with Interpolated Latent Features for Location Classification in Endoscopy Image and Video](https://mlsanity.com/paper/2103.08504/) (2021)
- [V2-SfMLearner: Learning Monocular Depth and Ego-motion for Multimodal Wireless Capsule Endoscopy](https://mlsanity.com/paper/2412.17595/) (2024)
- [CapsuleMotion: A Lightweight Real-Time Visual Motion Predictor for Capsule Endoscopy](https://mlsanity.com/paper/2609.15367/) (2026)

## Abstract (arXiv)

Ingestible wireless capsule endoscopy is an emerging minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies. Medical device companies and many research groups have recently made substantial progresses in converting passive capsule endoscopes to active capsule robots, enabling more accurate, precise, and intuitive detection of the location and size of the diseased areas. Since a reliable real time pose estimation functionality is crucial for actively controlled endoscopic capsule robots, in this study, we propose a monocular visual odometry (VO) method for endoscopic capsule robot operations. Our method lies on the application of the deep Recurrent Convolutional Neural Networks (RCNNs) for the visual odometry task, where Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are used for the feature extraction and inference of dynamics across the frames, respectively. Detailed analyses and evaluations made on a real pig stomach dataset proves that our system achieves high translational and rotational accuracies for different types of endoscopic capsule robot trajectories.