Topological localization using Wi-Fi and vision merged into FABMAP framework

Nowakowski, Mathieu; Joly, Cyril; Dalibard, Sébastien; Garcia, Nicolas; Moutarde, Fabien

doi:10.1109/iros.2017.8206171

Cited by 13 publications

(13 citation statements)

References 21 publications

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“…-We run our algorithm on four datasets from four different buildings to experimentally demonstrate the benefits of augmenting the three SLAM systems with Wi-Fi sensing on these four datasets. -We compare our work with the most recent stateof-the-art in WiFi-augmented visual sensing work which is Wi-Fi augmented FABMAP (Nowakowski et al, 2017).…”

Section: Computational Complexitymentioning

confidence: 99%

“…Therefore, FABMAP and its derivatives would perform poorly in direct localization or mapping error comparison. For a fairer comparison, we chose the exact same metrics as used in (Nowakowski et al, 2017). This is the CDF of distances between the estimated location and ground-truth averaged over the query images.…”

Section: Comparison To Wi-fi Augmented Fabmapmentioning

confidence: 99%

“…Algorithms reasoning with RGBD sensors come with some challenges when performing SLAM indoors. Some of the common problems are: Perceptual Aliasing (Xia et al, 2016;Nowakowski et al, 2017): Indoor environments tend to be symmetric and repetitive. Corridors with bland walls and repeated patterns of doors and lights could potentially cause confusion between different similar places (wrong loop closure) resulting in faulty maps and bad localization.…”

Section: Introductionmentioning

confidence: 99%

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Augmenting visual SLAM with Wi-Fi sensing for indoor applications

et al. 2019

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Recent trends have accelerated the development of spatial applications on mobile devices and robots. These include navigation, augmented reality, humanrobot interaction, and others. A key enabling technology for such applications is the understanding of the device's location and the map of the surrounding environment. This generic problem, referred to as Simultaneous Localization and Mapping (SLAM), is an extensively researched topic in robotics. However, visual SLAM algorithms face several challenges including perceptual aliasing and high computational cost. These challenges affect the accuracy, efficiency, and viability of visual SLAM algorithms, especially for long-term SLAM, and their use in resource-constrained mobile devices. A parallel trend is the ubiquity of Wi-Fi routers for quick Internet access in most urban environments. Most robots and mobile devices are equipped with a Wi-Fi radio as well. We propose a method to utilize Wi-Fi received signal strength to alleviate the challenges faced by visual SLAM algorithms. To demonstrate the utility of this idea, this work makes the following contributions: (i) We propose a generic way to integrate Wi-Fi sensing into visual SLAM algorithms, (ii) We integrate such sensing into three well-known SLAM algorithms, (iii) Using four distinct datasets, we demonstrate the performance of such augmentation in comparison to * the original visual algorithms and (iv) We compare our work to Wi-Fi augmented FABMAP algorithm. Overall, we show that our approach can improve the accuracy of visual SLAM algorithms by 11% on average and reduce computation time on average by 15% to 25%.

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Section: Computational Complexitymentioning

confidence: 99%

Section: Comparison To Wi-fi Augmented Fabmapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Augmenting visual SLAM with Wi-Fi sensing for indoor applications

et al. 2019

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“…FABMAP [6] uses Bayesian filtering to achieve long-term place recognition over 1000 km [6]. However, FABMAP cannot handle scenarios with variant changes in environmental conditions.…”

Section: Related Workmentioning

confidence: 99%

MRS-VPR: a multi-resolution sampling based global visual place recognition method

Yin

Srivatsan

Chen

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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Place recognition and loop closure detection are challenging for long-term visual navigation tasks. SeqSLAM is considered to be one of the most successful approaches to achieve long-term localization under varying environmental conditions and changing viewpoints. It depends on a bruteforce, time-consuming sequential matching method. We propose MRS-VPR, a multi-resolution, sampling-based place recognition method, which can significantly improve the matching efficiency and accuracy in sequential matching. The novelty of this method lies in the coarse-to-fine searching pipeline and a particle filter-based global sampling scheme, that can balance the matching efficiency and accuracy in the long-term navigation task. Moreover, our model works much better than SeqSLAM when the testing sequence has a much smaller scale than the reference sequence. Our experiments demonstrate that the proposed method is efficient in locating short temporary trajectories within long-term reference ones without losing accuracy compared to SeqSLAM.

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“…Many existing researches adopt local keypoints and global appearance by overcoming the differences of perspective in place recognition. FAB-MAP [5] and ORB-SLAM [6] utilize Bag-of-visual-Words (BoVW) [7] as place recognition module to encode each keyframe into an order-invariant vocabulary tree for local features [8], [9]. Appearance-based methods, such as NetVLAD [10] and SeqSLAM [11], compress single or continuous frame images into a global scene identifier to improve the robustness under perspective variations.…”

Section: Introductionmentioning

confidence: 99%

PSE-Match: A Viewpoint-free Place Recognition Method with Parallel Semantic Embedding

Yin

Xu²,

Egorov

et al. 2021

Preprint

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Accurate localization on the autonomous driving cars is essential for autonomy and driving safety, especially for complex urban streets and search-and-rescue subterranean environments where high-accurate GPS is not available. However current odometry estimation may introduce the drifting problems in long-term navigation without robust global localization. The main challenges involve scene divergence under the interference of dynamic environments and effective perception of observation and object layout variance from different viewpoints. To tackle these challenges, we present PSE-Match, a viewpoint-free place recognition method based on parallel semantic analysis of isolated semantic attributes from 3D point-cloud models. Compared with the original point cloud, the observed variance of semantic attributes is smaller. PSE-Match incorporates a divergence place learning network to capture different semantic attributes parallelly through the spherical harmonics domain. Using both existing benchmark datasets and two in-field collected datasets, our experiments show that the proposed method achieves above 70% average recall with top one retrieval and above 95% average recall with top ten retrieval cases. And PSE-Match has also demonstrated an obvious generalization ability with limited training dataset.

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Topological localization using Wi-Fi and vision merged into FABMAP framework

Cited by 13 publications

References 21 publications

Augmenting visual SLAM with Wi-Fi sensing for indoor applications

Augmenting visual SLAM with Wi-Fi sensing for indoor applications

MRS-VPR: a multi-resolution sampling based global visual place recognition method

PSE-Match: A Viewpoint-free Place Recognition Method with Parallel Semantic Embedding

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