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Optimizing Appearance-Based Localization with Catadioptric Cameras: Small-Footprint Models for Real-Time Inference on

Marta Rostkowska¹, Piotr Skrzypczyński¹

¹Institute of Robotics and Machine Intelligence, Poznan University of Technology, 60-965 Poznan, Poland.

Sensors (Basel, Switzerland)

|July 29, 2023

Summary

This summary is machine-generated.

This study presents an efficient neural network for visual place recognition using catadioptric cameras, enabling reliable indoor localization for mobile robots. The small-footprint model achieves real-time performance on edge devices, outperforming state-of-the-art systems.

Keywords:

deep learning edge computing localization mobile robot omnidirectional vision

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Appearance-based localization is crucial for mobile robot navigation.
Catadioptric cameras provide omnidirectional views but introduce image distortions.
Recognizing places in visually similar indoor environments with limited features is challenging.

Purpose of the Study:

To design an efficient neural network architecture for appearance-based localization using catadioptric images.
To develop small-footprint models for real-time inference on edge devices for low-cost service robots.
To evaluate the system's accuracy and efficiency compared to state-of-the-art methods.

Main Methods:

Designing and comparing neural network architectures for visual place recognition.
Utilizing transfer learning and fine-tuning on catadioptric images to generate global descriptors (embeddings).
Testing on custom datasets and publicly available datasets (COLD Freiburg, Saarbrücken).

Main Results:

The best results were achieved using embeddings from transfer learning and fine-tuning.
The proposed system demonstrated favorable accuracy in place recognition.
The system achieved competitive inference times, suitable for real-time applications.

Conclusions:

The developed system offers a cost- and energy-efficient solution for appearance-based localization.
The approach is effective for indoor service robots using catadioptric cameras.
The optimized neural network architecture enables reliable localization even in challenging environments.