Original Research Article

Vision-Based Self-Mapping for Autonomous Indoor Drones

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Abstract

Objectives:

Without GPS signals the problem of self-autonomous navigation of indoor drones is greatly complicated and

consequently there has been a recent surge of interest in Visual SLAM. Such systems enable the localization of a

drone in an environment and the building of a map of it using only camera data. However indoor environments

generally pose severe problems for these systems such as low illumination, motion blur due to rapid motion and

featureless surfaces that do not offer the reliable features required for tracking. Severity of such -problems impairs,

in extreme cases, the spatial orientation and the information of the place, which decreases localization quality and

the robustness. The goal of this thesis is to develop an adaptive hybrid image enhancement technique to satisfy

the real-time and localization accuracy criteria of visual SLAM based indoor drone navigation.

Material and Methods:

The system combines the simple preprocessing methods, Gamma Correction and Contrast Limited Adaptive

Histogram Equalization (CLAHE), with a particular enhancement model based on Convolutional Neural Networks

(CNNs). When the Decision Tree classifier determines that the coverage of brightness, sharpness and feature

density is such that advanced enhancement is required, advanced enhancement procedures are activated. This

system is implemented as an external preprocessing component for ORB-SLAM3 and does not require

modifications to the preprocessing pipeline of ORB-SLAM3. Evaluation was performed on two orthogonal data

sets EuRoC MH01 and TartanAir P001. The evaluation of localization was performed using the two metrics,

Absolute Trajectory Error (ATE) and Relative Pose Error (RPE), obtained with help of the evo toolkit.

Results:

The strategy shown had a drastic reduction in the ATE with EuRoC MH01, with the ATE of 1.431 m down to the

0.978 m representing a 31.7% reduction in ATE. Moreover, the difficult TartanAir P001 test resulted in a great

reduction in the mean error, from 6.667 m to 0.510 m, which is a 92.3% reduction. The Decision Tree classifier

had a very high accuracy of 99.97% and a recall of 1, and only 0.41% of the images were passed through the CNN

enhancement module. Within the adaptive framework enhancement preprocessing was processed at a rate of

696.67 frames per second (FPS) while in the continuous CNN the enhancement preprocessing rate was only at

69.99 FPS which is a 92.06% decrease in processing time.

Conclusion:

Hence the adaptive framework integrates localization precision and computational efficiency and is therefore

suitable for real time indoor drone navigation. Selective enhancement greatly reduces the computational cost and

maintains good indoor localization performance in different indoor environments. Future studies may explore

implementation of the presented framework on embedded systems and its application to stereo and visual-inertial

SLAM systems.

Keywords:

• Visual SLAM,
• ORB-SLAM3,
• Image Enhancement,
• Indoor Drone Navigation,
• Decision Tree,
• Low-Light Vision,
• Adaptive Processing