Bio-inspired Multi-scale Visual Place Recognition for the Aerial Vehicle Navigation

Multiscale Convolutional Descriptor Aggregation for Visual Place Recognition

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.10.ipas-312 ◽

2020 ◽

Vol 2020 (10) ◽

pp. 313-1-313-7

Author(s):

Raffaele Imbriaco ◽

Egor Bondarev ◽

Peter H.N. de With

Keyword(s):

Image Matching ◽

Large Scale ◽

Place Recognition ◽

Aggregation Method ◽

Local Descriptors ◽

Multi Scale ◽

Vector Construction ◽

Visual Place Recognition ◽

Different Sources ◽

Geometric Verification

Visual place recognition using query and database images from different sources remains a challenging task in computer vision. Our method exploits global descriptors for efficient image matching and local descriptors for geometric verification. We present a novel, multi-scale aggregation method for local convolutional descriptors, using memory vector construction for efficient aggregation. The method enables to find preliminary set of image candidate matches and remove visually similar but erroneous candidates. We deploy the multi-scale aggregation for visual place recognition on 3 large-scale datasets. We obtain a Recall@10 larger than 94% for the Pittsburgh dataset, outperforming other popular convolutional descriptors used in image retrieval and place recognition. Additionally, we provide a comparison for these descriptors on a more challenging dataset containing query and database images obtained from different sources, achieving over 77% Recall@10.

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Real-Time Visual Place Recognition Based on Analyzing Distribution of Multi-scale CNN Landmarks

Journal of Intelligent & Robotic Systems ◽

10.1007/s10846-018-0804-x ◽

2018 ◽

Vol 94 (3-4) ◽

pp. 777-792 ◽

Cited By ~ 6

Author(s):

Zhe Xin ◽

Xiaoguang Cui ◽

Jixiang Zhang ◽

Yiping Yang ◽

Yanqing Wang

Keyword(s):

Real Time ◽

Place Recognition ◽

Multi Scale ◽

Visual Place Recognition

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Appearance and viewpoint invariant visual place recognition using multi-scale and multi-modality systems

10.5204/thesis.eprints.226953 ◽

2021 ◽

Author(s):

Stephen D Hausler

Keyword(s):

Place Recognition ◽

Multi Scale ◽

Visual Place Recognition

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

Applied Sciences ◽

10.3390/app9153146 ◽

2019 ◽

Vol 9 (15) ◽

pp. 3146 ◽

Cited By ~ 1

Author(s):

Bo Yang ◽

Xiaosu Xu ◽

Jun Li ◽

Hong Zhang

Keyword(s):

Recognition Performance ◽

Input Parameter ◽

Sliding Window ◽

Recognition System ◽

Place Recognition ◽

Size Distributions ◽

Multi Scale ◽

Shift Invariance ◽

Single Input ◽

Visual Place Recognition

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.

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