Landmark Generation in Visual Place Recognition Using Multi-Scale Sliding Window for Robotics

Bo Yang,Hong Zhang,Jun Li,Xiaosu Xu

doi:10.3390/app9153146

Bo Yang, Hong Zhang + Show 2 more

Open Access

https://doi.org/10.3390/app9153146

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Abstract

Landmark generation is an essential component in landmark-based visual place recognition. In this paper, we present a simple yet effective method, called multi-scale sliding window (MSW), for landmark generation in order to improve the performance of place recognition. In our method, we generate landmarks that form a uniform distribution in multiple landmark scales (sizes) within an appropriate range by a process that samples an image with a sliding window. This is in contrast to conventional methods of landmark generation that typically depend on detecting objects whose size distributions are uneven and, as a result, may not be effective in achieving shift invariance and viewpoint invariance, two important properties in visual place recognition. We conducted experiments on four challenging datasets to demonstrate that the recognition performance can be significantly improved by our method in a standard landmark-based visual place recognition system. Our method is simple with a single input parameter, the scales of landmarks required, and it is efficient as it does not involve detecting objects.

Highlights

Visual place recognition plays a critical role in both long-term robotic autonomy [1,2,3,4,5,6,7] and spatial cognition and cartography [8,9]
The primary contribution of the research described in this paper is to demonstrate that multi-scale sliding window (MSW) is an efficient and effective method for generating landmark for convolutional neural network (CNN)-based visual place recognition, superior to standard object detection methods popularly used in the literature
We propose a novel method for generating landmarks for landmark-based visual place recognition systems

Summary

Introduction

Visual place recognition plays a critical role in both long-term robotic autonomy [1,2,3,4,5,6,7] and spatial cognition and cartography [8,9]. It helps determine whether the current view of a robot corresponds to a place or location that has been already visited in the past [4], and benefits the usability of landmark pictograms in the field of cartography [9]. Seasonal changes in an outdoor environment can adversely affect the recognition performance as well [3]

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