scholarly journals A Robust Indoor Localization System Integrating Visual Localization Aided by CNN-Based Image Retrieval with Monte Carlo Localization

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 249 ◽  
Author(s):  
Song Xu ◽  
Wusheng Chou ◽  
Hongyi Dong
Keyword(s):  
Monte Carlo ◽  
Image Retrieval ◽  
Closed Loop ◽  
Short Term Memory ◽  
Visual Localization ◽  
Place Recognition ◽  
Global Localization ◽  
Localization System ◽  
Monte Carlo Localization ◽  
Fine Localization

This paper proposes a novel multi-sensor-based indoor global localization system integrating visual localization aided by CNN-based image retrieval with a probabilistic localization approach. The global localization system consists of three parts: coarse place recognition, fine localization and re-localization from kidnapping. Coarse place recognition exploits a monocular camera to realize the initial localization based on image retrieval, in which off-the-shelf features extracted from a pre-trained Convolutional Neural Network (CNN) are adopted to determine the candidate locations of the robot. In the fine localization, a laser range finder is equipped to estimate the accurate pose of a mobile robot by means of an adaptive Monte Carlo localization, in which the candidate locations obtained by image retrieval are considered as seeds for initial random sampling. Additionally, to address the problem of robot kidnapping, we present a closed-loop localization mechanism to monitor the state of the robot in real time and make adaptive adjustments when the robot is kidnapped. The closed-loop mechanism effectively exploits the correlation of image sequences to realize the re-localization based on Long-Short Term Memory (LSTM) network. Extensive experiments were conducted and the results indicate that the proposed method not only exhibits great improvement on accuracy and speed, but also can recover from localization failures compared to two conventional localization methods.

scholarly journals ERF-IMCS: An Efficient and Robust Framework with Image-Based Monte Carlo Scheme for Indoor Topological Navigation

2020 ◽  
Vol 10 (19) ◽  
pp. 6829
Author(s):  
Song Xu ◽  
Huaidong Zhou ◽  
Wusheng Chou
Keyword(s):  
Monte Carlo ◽  
Image Retrieval ◽  
Large Scale ◽  
Visual Localization ◽  
Global Localization ◽  
Localization Method ◽  
Perceptual Aliasing ◽  
Topological Localization ◽  
Global And Local ◽  
Topological Navigation

Conventional approaches to global localization and navigation mainly rely on metric maps to provide precise geometric coordinates, which may cause the problem of large-scale structural ambiguity and lack semantic information of the environment. This paper presents a scalable vision-based topological mapping and navigation method for a mobile robot to work robustly and flexibly in large-scale environment. In the vision-based topological navigation, an image-based Monte Carlo localization method is presented to realize global topological localization based on image retrieval, in which fine-tuned local region features from an object detection convolutional neural network (CNN) are adopted to perform image matching. The combination of image retrieval and Monte Carlo provide the robot with the ability to effectively avoid perceptual aliasing. Additionally, we propose an effective visual localization method, simultaneously employing the global and local CNN features of images to construct discriminative representation for environment, which makes the navigation system more robust to the interference of occlusion, translation, and illumination. Extensive experimental results demonstrate that ERF-IMCS exhibits great performance in the robustness and efficiency of navigation.

scholarly journals Self-adaptive Monte Carlo localization for mobile robots using range finders

Robotica ◽  
2011 ◽  
Vol 30 (2) ◽  
pp. 229-244 ◽  
Author(s):  
Lei Zhang ◽  
René Zapata ◽  
Pascal Lépinay
Keyword(s):  
Monte Carlo ◽  
Mobile Robots ◽  
Position Tracking ◽  
Localization Algorithm ◽  
Global Localization ◽  
Adaptive Monte Carlo ◽  
Local Sample ◽  
Monte Carlo Localization ◽  
Sample Set ◽  
Self Adaptive

SUMMARYIn order to achieve the autonomy of mobile robots, effective localization is a necessary prerequisite. In this paper, we propose an improved Monte Carlo localization algorithm using self-adaptive samples (abbreviated as SAMCL). By employing a pre-caching technique to reduce the online computational burden, SAMCL is more efficient than the regular MCL. Further, we define the concept of similar energy region (SER), which is a set of poses (grid cells) having similar energy with the robot in the robot space. By distributing global samples in SER instead of distributing randomly in the map, SAMCL obtains a better performance in localization. Position tracking, global localization and the kidnapped robot problem are the three sub-problems of the localization problem. Most localization approaches focus on solving one of these sub-problems. However, SAMCL solves all the three sub-problems together, thanks to self-adaptive samples that can automatically separate themselves into a global sample set and a local sample set according to needs. The validity and the efficiency of the SAMCL algorithm are demonstrated by both simulations and experiments carried out with different intentions. Extensive experimental results and comparisons are also given in this paper.

Mobile robot Monte-Carlo localization using image retrieval

2008 ◽  
Author(s):  
Fengda Zhao ◽  
Lingfu Kong ◽  
Peiliang Wu ◽  
Kaiyuan Fu
Keyword(s):  
Monte Carlo ◽  
Mobile Robot ◽  
Image Retrieval ◽  
Monte Carlo Localization

Enhanced Monte Carlo Localization with Visual Place Recognition for Robust Robot Localization

2015 ◽  
Vol 80 (3-4) ◽  
pp. 641-656 ◽  
Author(s):  
Javier Pérez ◽  
Fernando Caballero ◽  
Luis Merino
Keyword(s):  
Monte Carlo ◽  
Robot Localization ◽  
Place Recognition ◽  
Monte Carlo Localization ◽  
Visual Place Recognition
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