Deep UnDeep Visual Inertial Odometry

Phase 1 : Classical Approach to Visual Inertial Odometry

Introduction

This project provides a Python reimplementation of the Stereo Multi-State Constraint Kalman Filter (S-MSCKF) for visual-inertial odometry. This implementation has been inspired from the C++ implementation by KumarRobotics/msckf_vio.

Algorithm Overview

MSCKF (Multi-State Constraint Kalman Filter) is an Extended Kalman Filter (EKF) based, tightly-coupled visual-inertial odometry algorithm. S-MSCKF is the stereo version of MSCKF. This algorithm is designed to provide robust and accurate visual-inertial odometry, especially for applications requiring fast and autonomous flight.

Key Papers:

• Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight by Ke Sun et al. (2017)
• A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation by Anastasios I. Mourikis et al. (2006)

Requirements

• Python 3.6+
• numpy
• scipy
• cv2
• pangolin (optional, for trajectory/poses visualization)

Dataset

• This project utilizes the EuRoC MAV dataset, which contains visual-inertial datasets collected onboard a Micro Aerial Vehicle (MAV). The datasets include stereo images, synchronized IMU measurements, and ground-truth data.

Dataset Details:

• EuRoC MAV: The dataset can be found here.

Running the Code

• To run the visual-inertial odometry algorithm with visualization, use the following command:

  python vio.py --view --path path/to/your/EuRoC_MAV_dataset/MH_01_easy
  

• For running the algorithm without visualization, use:

  python vio.py --path path/to/your/EuRoC_MAV_dataset/MH_01_easy
  

• Final Output

Phase 2: Deep Learning Approach to Visual Inertial Odometry

Problem Statement

Dataset Generation using Blender

Networks Used

Deep Visual Odometry Network

Deep Inertial Odometry Network

Deep Visual Inertial Odometry Network

Final Output [will be uploaded soon]