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SymbioLCD - Datasets

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Overview:
Three new datasets available here represent normal household areas with common objects - lounge, kitchen and garden - with varying trajectories.


Description:
Lounge: The lounge dataset with common household objects.
Lounge_oc: The lounge dataset with object occlusions near the end of trajectory.
Kitchen: The kitchen dataset with common household objects.
Kitchen_oc: The kitchen dataset with object occlusions near the end of trajectory.
Garden: The garden dataset with common household objects.
Garden_oc: The garden dataset with object occlusions near the end of trajectory.
convert.py: Python script to convert a video file into jpgs.


Paper:
The datasets were used for the paper "SymbioLCD: Ensemble-Based Loop Closure Detection using CNN-Extracted Objects and Visual Bag-of-Words", accepted at 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems.


Abstract:
Loop closure detection is an essential tool of Simultaneous Localization and Mapping (SLAM) to minimize drift in its localization. Many state-of-the-art loop closure detection (LCD) algorithms use visual Bag-of-Words (vBoW), which is robust against partial occlusions in a scene but cannot perceive the semantics or spatial relationships between feature points. CNN object extraction can address those issues, by providing semantic labels and spatial relationships between objects in a scene. Previous work has mainly focused on replacing vBoW with CNN derived features.
In this paper we propose SymbioLCD, a novel ensemble-based LCD that utilizes both CNN-extracted objects and vBoW features for LCD candidate prediction. When used in tandem, the added elements of object semantics and spatial-awareness creates a more robust and symbiotic loop closure detection system. The proposed SymbioLCD uses scale-invariant spatial and semantic matching, Hausdorff distance with temporal constraints, and a Random Forest that utilizes combined information from both CNN-extracted objects and vBoW features for predicting accurate loop closure candidates. Evaluation of the proposed method shows it outperforms other Machine Learning (ML) algorithms - such as SVM, Decision Tree and Neural Network, and demonstrates that there is a strong symbiosis between CNN-extracted object information and vBoW features which assists accurate LCD candidate prediction. Furthermore, it is able to perceive loop closure candidates earlier than state-of-the-art SLAM algorithms, utilizing added spatial and semantic information from CNN-extracted objects.

Citation:
Please use the bibtex below for citing the paper:

@inproceedings{kim2021symbiolcd,
title = {SymbioLCD: Ensemble-Based Loop Closure Detection using CNN-Extracted Objects and Visual Bag-of-Words},
author = {Jonathan Kim and Martin Urschler and Pat Riddle and J\"{o}rg Wicker},
year = {2021},
date = {2021-09-27},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}

Funding

Callaghan Innovation Strategic Investment

Ministry of Business, Innovation and Employment

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Publisher

University of Auckland

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