Pedestrian Localization on Topological Maps with Neural Machine Translation Network

Wei, Jianli; Koroglu, M. Taha; Zha, Bing; Yilmaz, Alper

doi:10.1109/sensors43011.2019.8956924

Cited by 6 publications

(7 citation statements)

References 24 publications

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“…Some studies have focused on estimation correction using classification techniques. For example, in [156] and [157], the authors use RNN for pedestrian localization in topological digital maps. They adapt a LSTM neural network, which is used in language translation, to the pedestrian localization process.…”

Section: E Machine Learning In Fusion Frameworkmentioning

confidence: 99%

A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

et al. 2021

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With the popularization of machine learning (ML) techniques and the increased chipset's performance, the application of ML to pedestrian localization systems has received significant attention in the last years. Several survey papers have attempted to provide a state-of-the-art overview, but they usually limit their scope to a particular type of positioning system or technology. In addition, they are written from the point of view of ML techniques and their practice, not from the point of view of the localization system and the specific problems that ML techniques can help to solve. This article is intended to offer a comprehensive state-of-the-art survey of the ML techniques that have been adopted over the last ten years to improve the performance of pedestrian localization systems, addressing the applicability of ML techniques in this domain, along with the main localization strategies. It concludes by indicating the underlying open issues and challenges associated with the existing systems, and possible future directions in which ML techniques could improve the performance of pedestrian localization systems. Among other open issues, most previous authors have focused their attention on position estimation accuracy, which wastes the potential of ML techniques to improve other performance parameters (e.g., response time, computational complexity, robustness, scalability or energy efficiency). This study shows that there is a strong trend towards the application of supervised learning. Consequently, there are many potential research opportunities in the use of other learning types, such as unsupervised and reinforcement learning, to improve the performance of pedestrian localization systems.

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Section: E Machine Learning In Fusion Frameworkmentioning

confidence: 99%

A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

et al. 2021

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“…(Gupta et al, 2016) presented an object localization approach based on stochastic trajectory matching using a brute-force location search which is time consuming in large maps. In (Wei et al, 2019), a sequence to sequence labeling method for trajectory matching using neural machine translation network is proposed. This approach, however, was shown to work well on synthetic scenarios where the input trajectory had no errors, while in reality all dead reckoning methods suffer from drift problems.…”

Section: Visual Localization and Deep Localizationmentioning

confidence: 99%

“…This approach, however, was shown to work well on synthetic scenarios where the input trajectory had no errors, while in reality all dead reckoning methods suffer from drift problems. In another paper, authors of (Zha et al, 2019) used similar ideas to (Wei et al, 2019) and showed their performance on synthetic trajectories.…”

Section: Visual Localization and Deep Localizationmentioning

confidence: 99%

Learning Maps for Object Localization Using Visual-Inertial Odometry

Zha

Yilmaz

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Objects follow designated path on maps, such as vehicles travelling on a road. This observation signifies topological representation of objects’ motion on the map. Considering the position of object is unknown initially, as it traverses the map by moving and turning, the spatial uncertainty of its whereabouts reduces to a single location as the motion trajectory would fit only to a certain map trajectory. Inspired by this observation, we propose a novel end-to-end localization approach based on topological maps that exploits the object motion and learning the map using an recurrent neural network (RNN) model. The core of the proposed method is to learn potential motion patterns from the map and perform trajectory classification in the map’s edge-space. Two different trajectory representations, namely angle representation and augmented angle representation (incorporates distance traversed) are considered and an RNN is trained from the map for each representation to compare their performances. The localization accuracy in the tested map for the angle and augmented angle representations are 90.43% and 96.22% respectively. The results from the actual visual-inertial odometry have shown that the proposed approach is able to learn the map and localize objects based on their motion.

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“…Localization from Deep Learning. There are a few similar studies closely related to us using deep learning [23], [24] . In [23], a sequence to sequence labeling method for trajectory matching using neural machine translation network is proposed.…”

Section: Related Workmentioning

confidence: 99%

“…There are a few similar studies closely related to us using deep learning [23], [24] . In [23], a sequence to sequence labeling method for trajectory matching using neural machine translation network is proposed. This approach was shown to only work well on synthetic scenarios where the input trajectory was synthetically generated with known sequence of nodes from the map.…”

Section: Related Workmentioning

confidence: 99%

Map-Based Temporally Consistent Geolocalization through Learning Motion Trajectories

Zha

Yilmaz

2020

Preprint

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In this paper, we propose a novel trajectory learning method that exploits motion trajectories on topological map using recurrent neural network for temporally consistent geolocalization of object. Inspired by human's ability to both be aware of distance and direction of self-motion in navigation, our trajectory learning method learns a pattern representation of trajectories encoded as a sequence of distances and turning angles to assist self-localization. We pose the learning process as a conditional sequence prediction problem in which each output locates the object on a traversable path in a map. Considering the prediction sequence ought to be topologically connected in the graph-structured map, we adopt two different hypotheses generation and elimination strategies to eliminate disconnected sequence prediction. We demonstrate our approach on the KITTI stereo visual odometry dataset which is a city-scale environment and can generate trajectory with metric information. The key benefits of our approach to geolocalization are that 1) we take advantage of powerful sequence modeling ability of recurrent neural network and its robustness to noisy input, 2) only require a map in the form of a graph and simply use an affordable sensor that generates motion trajectory and 3) do not need initial position. The experiments show that the motion trajectories can be learned by training an recurrent neural network, and temporally consistent geolocation can be predicted with both of the proposed strategies.

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Pedestrian Localization on Topological Maps with Neural Machine Translation Network

Cited by 6 publications

References 24 publications

A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

Learning Maps for Object Localization Using Visual-Inertial Odometry

Map-Based Temporally Consistent Geolocalization through Learning Motion Trajectories

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