# Vision-Depth Landmarks and Inertial Fusion for Navigation in Degraded Visual Environments

## TL;DR

This paper introduces a fusion method combining visual, depth, and inertial data to enable robust robot navigation in GPS-denied, poorly lit, and texture-less environments, demonstrated through experiments with handheld and aerial robots.

## Contribution

It presents a novel tight fusion approach of multimodal sensory data at feature and sensor levels for enhanced navigation in degraded visual environments.

## Key findings

- Reliable performance in challenging environments
- Effective fusion of RGB-D and inertial data
- Successful experiments with handheld and aerial robots

## Abstract

This paper proposes a method for tight fusion of visual, depth and inertial data in order to extend robotic capabilities for navigation in GPS-denied, poorly illuminated, and texture-less environments. Visual and depth information are fused at the feature detection and descriptor extraction levels to augment one sensing modality with the other. These multimodal features are then further integrated with inertial sensor cues using an extended Kalman filter to estimate the robot pose, sensor bias terms, and landmark positions simultaneously as part of the filter state. As demonstrated through a set of hand-held and Micro Aerial Vehicle experiments, the proposed algorithm is shown to perform reliably in challenging visually-degraded environments using RGB-D information from a lightweight and low-cost sensor and data from an IMU.