A Robot Localization Framework Using CNNs for Object Detection and Pose Estimation

Hoyer, Lukas; Steup, Christoph; Mostaghim, Sanaz

doi:10.1109/ssci.2018.8628752

Cited by 7 publications

(5 citation statements)

References 28 publications

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“…In [ 33 ], the authors made a comprehensive review of existing computer vision-based indoor localization methods. In [ 34 ], the authors proposed a localization framework, where a convolutional neural network (CNN) was employed to observe the type and bounding box of the target; another neural network was employed for orientation and distance calculations. Moreover, simultaneous localization and mapping (SLAM) [ 35 ] has received more attention recently; it is capable of constructing maps, positioning, and detecting indoor static objects in real time.…”

Section: Related Workmentioning

confidence: 99%

Co-Occurrence Fingerprint Data-Based Heterogeneous Transfer Learning Framework for Indoor Positioning

Huang

Guo

et al. 2022

Sensors

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Distribution discrepancy is an intrinsic challenge in existing fingerprint-based indoor positioning system(s) (FIPS) due to real-time environmental variations; thus, the positioning model needs to be reconstructed frequently based on newly collected training data. However, it is expensive or impossible to collect adequate training samples to reconstruct the fingerprint database. Fortunately, transfer learning has proven to be an effective solution to mitigate the distribution discrepancy, enabling us to update the positioning model using newly collected training data in real time. However, in practical applications, traditional transfer learning algorithms no longer act well to feature space heterogeneity caused by different types or holding postures of fingerprint collection devices (such as smartphones). Moreover, current heterogeneous transfer methods typically require enough accurately labeled samples in the target domain, which is practically expensive and even unavailable. Aiming to solve these problems, a heterogeneous transfer learning framework based on co-occurrence data (HTL-CD) is proposed for FIPS, which can realize higher positioning accuracy and robustness against environmental changes without reconstructing the fingerprint database repeatedly. Specifically, the source domain samples are mapped into the feature space in the target domain, then the marginal and conditional distributions of the source and target samples are aligned in order to minimize the distribution divergence caused by collection device heterogeneity and environmental changes. Moreover, the utilized co-occurrence fingerprint data enables us to calculate correlation coefficients between heterogeneous samples without accurately labeled target samples. Furthermore, by resorting to the adopted correlation restriction mechanism, more valuable knowledge will be transferred to the target domain if the source samples are related to the target ones, which remarkably relieves the “negative transfer" issue. Real-world experimental performance implies that, even without accurately labeled samples in the target domain, the proposed HTL-CD can obtain at least 17.15% smaller average localization errors (ALEs) than existing transfer learning-based positioning methods, which further validates the effectiveness and superiority of our algorithm.

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Section: Related Workmentioning

confidence: 99%

Co-Occurrence Fingerprint Data-Based Heterogeneous Transfer Learning Framework for Indoor Positioning

Huang

Guo

et al. 2022

Sensors

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“…The performance of the Vicon system comes at a very high cost of typically more than EUR 50,000. Other approaches are based on single cameras and use no markers, such as that in [13]. These provide far less precision (typically 10 cm, but also with a high update rate of 50 Hz or more).…”

Section: Related Workmentioning

confidence: 99%

A Single-Copter UWB-Ranging-Based Localization System Extendable to a Swarm of Drones

Steup

Beckhaus

Mostaghim

2021

Drones

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This paper presents a single-copter localization system as a first step towards a scalable multihop drone swarm localization system. The drone was equipped with ultrawideband (UWB) transceiver modules, which can be used for communication, as well as distance measurement. The location of the drone was detected based on fixed anchor points using a single type of UWB transceiver. Our aim is to create a swarm localization system that enables drones to switch their role between an active swarm member and an anchor node to enhance the localization of the whole swarm. To this end, this paper presents our current baseline localization system and its performance regarding single-drone localization with fixed anchors and its integration into our current modular quadcopters, which was designed to be easily extendable to a swarm localization system. The distance between each drone and the anchors was measured periodically, and a specially tailored gradient descent algorithm was used to solve the resulting nonlinear optimization problem. Additional copter and wireless-specific adaptations were performed to enhance the robustness. The system was tested with a Vicon system as a position reference and showed a high precision of 0.2 m with an update rate of <10 Hz. Additionally, the system was integrated into the FINken copters of the SwarmLab and evaluated in multiple outdoor scenarios. These scenarios showed the generic usability of the approach, even though no accurate precision measurement was possible.

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“…It employs Mask R-CNN and OpenPose [128] to detect people and their pose (standing, sitting) and the perspective transformation to obtain the position of the users on a map. Hoyer et al [71] presented a localization framework for robots based on Convolutional Neural Networks (CNN) using static cameras. In a first stage, they used a CNN object detection to estimate the type and the bounding box of a robot.…”

Section: Indoor Localization Solutions With 2d Static Cameras Markers...mentioning

confidence: 99%

“…Table 4 presents the characteristics of the localization methods that use 2D static cameras, real features, and artificial intelligence based algorithms. The computational challenge of using neural networks or other AI based implementations can be met with the use of GPUs, as can be observed for several methods [71,73], which achieve interactive or real-time performance. Although a higher complexity of the algorithms would lead to expecting a higher accuracy level compared to the previous class of solutions, relevant accuracy comparisons cannot be made due to the evaluations being performed on different datasets/scenarios.…”

Section: Indoor Localization Solutions With 2d Static Cameras Markers...mentioning

confidence: 99%

A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision

Morar

Moldoveanu

Mocanu

et al. 2020

Sensors

112

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Computer vision based indoor localization methods use either an infrastructure of static cameras to track mobile entities (e.g., people, robots) or cameras attached to the mobile entities. Methods in the first category employ object tracking, while the others map images from mobile cameras with images acquired during a configuration stage or extracted from 3D reconstructed models of the space. This paper offers an overview of the computer vision based indoor localization domain, presenting application areas, commercial tools, existing benchmarks, and other reviews. It provides a survey of indoor localization research solutions, proposing a new classification based on the configuration stage (use of known environment data), sensing devices, type of detected elements, and localization method. It groups 70 of the most recent and relevant image based indoor localization methods according to the proposed classification and discusses their advantages and drawbacks. It highlights localization methods that also offer orientation information, as this is required by an increasing number of applications of indoor localization (e.g., augmented reality).

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A Robot Localization Framework Using CNNs for Object Detection and Pose Estimation

Cited by 7 publications

References 28 publications

Co-Occurrence Fingerprint Data-Based Heterogeneous Transfer Learning Framework for Indoor Positioning

Co-Occurrence Fingerprint Data-Based Heterogeneous Transfer Learning Framework for Indoor Positioning

A Single-Copter UWB-Ranging-Based Localization System Extendable to a Swarm of Drones

A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision

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