SymbioLCD - Datasets

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}}

概览：本平台提供三个新的数据集，分别代表具有不同轨迹的普通住宅区域，包括客厅、厨房和花园。描述：客厅数据集包含常见的家庭用品；客厅遮挡数据集在轨迹末尾附近存在物品遮挡；厨房数据集包含常见的家庭用品；厨房遮挡数据集在轨迹末尾附近存在物品遮挡；花园数据集包含常见的家庭用品；花园遮挡数据集在轨迹末尾附近存在物品遮挡。convert.py：Python脚本，用于将视频文件转换为jpg图片。论文：这些数据集被用于论文《SymbioLCD：基于集成学习的使用CNN提取对象和视觉词汇袋的环路闭合检测》，该论文已被2021年IEEE/RSJ国际机器人与系统会议接受。摘要：环路闭合检测是同步定位与地图构建（SLAM）中的一项基本工具，用于最小化定位过程中的漂移。许多最先进的环路闭合检测（LCD）算法使用视觉词汇袋（vBoW），对场景中的部分遮挡具有鲁棒性，但无法感知特征点之间的语义或空间关系。CNN对象提取可以通过提供场景中对象的语义标签和空间关系来解决这个问题。先前的研究主要集中于用CNN提取的特征替换vBoW。在本文中，我们提出了SymbioLCD，这是一种新型的基于集成的LCD，它利用CNN提取的对象和vBoW特征进行LCD候选预测。当两者结合使用时，对象语义和空间感知性的增加创造了一个更加强大且相互依存的环路闭合检测系统。所提出的SymbioLCD使用尺度不变的空间和语义匹配、带时间约束的Hausdorff距离以及一个利用CNN提取的对象和vBoW特征结合信息的随机森林来预测准确的环路闭合候选。该方法的效果优于其他机器学习（ML）算法，如支持向量机（SVM）、决策树和神经网络，并证明了CNN提取的对象信息和vBoW特征之间存在强烈的共生关系，这有助于准确预测LCD候选。此外，它能够比最先进的SLAM算法更早地感知环路闭合候选，利用CNN提取对象提供的额外空间和语义信息。引用：请使用以下bibtex进行论文引用：@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}}