A CNN Regression Approach to Mobile Robot Localization Using Omnidirectional Images

Ballesta, M. Teresa Sánchez; Payá, Luis; Cebollada, Sergio; Reinoso, Óscar; Murcia, Francisco

doi:10.3390/app11167521

Cited by 11 publications

(4 citation statements)

References 45 publications

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“…The V-Rep 3.2.1 simulation software was used for the validation of the proposed method. In [26], the authors described the application of the convolutional neural network (CNN) for the robot localization problem. The issue was solved using a hierarchical approach.…”

Section: Regression Methods For Robot Localizationmentioning

confidence: 99%

A Survey of Machine Learning Approaches for Mobile Robot Control

Rybczak,

Popowniak,

Lazarowska

2024

Robotics

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Machine learning (ML) is a branch of artificial intelligence that has been developing at a dynamic pace in recent years. ML is also linked with Big Data, which are huge datasets that need special tools and approaches to process them. ML algorithms make use of data to learn how to perform specific tasks or make appropriate decisions. This paper presents a comprehensive survey of recent ML approaches that have been applied to the task of mobile robot control, and they are divided into the following: supervised learning, unsupervised learning, and reinforcement learning. The distinction of ML methods applied to wheeled mobile robots and to walking robots is also presented in the paper. The strengths and weaknesses of the compared methods are formulated, and future prospects are proposed. The results of the carried out literature review enable one to state the ML methods that have been applied to different tasks, such as the following: position estimation, environment mapping, SLAM, terrain classification, obstacle avoidance, path following, learning to walk, and multirobot coordination. The survey allowed us to associate the most commonly used ML algorithms with mobile robotic tasks. There still exist many open questions and challenges such as the following: complex ML algorithms and limited computational resources on board a mobile robot; decision making and motion control in real time; the adaptability of the algorithms to changing environments; the acquisition of large volumes of valuable data; and the assurance of safety and reliability of a robot’s operation. The development of ML algorithms for nature-inspired walking robots also seems to be a challenging research issue as there exists a very limited amount of such solutions in the recent literature.

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Section: Regression Methods For Robot Localizationmentioning

confidence: 99%

A Survey of Machine Learning Approaches for Mobile Robot Control

Rybczak,

Popowniak,

Lazarowska

2024

Robotics

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“…In the article of Glaeser et al (2018) ConvNet is used to evaluate the impact of the exterior and, in Poursaeed et al (2018), the interior visual appearance of a building on prices. The use of ConvNets for regression is not as widespread as for classification problems, but is increasingly gaining application such as for position recognition in buildings (Ballesta et al, 2021). The regression ConvNet methodology is also used for predicting stock prices via annual reports and text analysis (Dereli and Saraclar, 2019) and using historical data (Mehtab and Sen, 2020).…”

Section: Deep Learning For Visual Pattern Recognitionmentioning

confidence: 99%

“…(2018), the interior visual appearance of a building on prices. The use of ConvNets for regression is not as widespread as for classification problems, but is increasingly gaining application such as for position recognition in buildings (Ballesta et al. , 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Leveraging supplementary modalities in automated real estate valuation using comparative judgments and deep learning

Despotović

Koch

Stumpe

et al. 2023

JERER

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PurposeIn this study the authors aim to outline new ways of information extraction for automated valuation models, which in turn would help to increase transparency in valuation procedures and thus contribute to more reliable statements about the value of real estate.Design/methodology/approachThe authors hypothesize that empirical error in the interpretation and qualitative assessment of visual content can be minimized by collating the assessments of multiple individuals and through use of repeated trials. Motivated by this problem, the authors developed an experimental approach for semi-automatic extraction of qualitative real estate metadata based on Comparative Judgments and Deep Learning. The authors evaluate the feasibility of our approach with the help of Hedonic Models.FindingsThe results show that the collated assessments of qualitative features of interior images show a notable effect on the price models and thus over potential for further research within this paradigm.Originality/valueTo the best of the authors’ knowledge, this is the first approach that combines and collates the subjective ratings of visual features and deep learning for real estate use cases.

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“…As the construction of invariant feature descriptors for omnidirectional images is problematic, Masci et al [ 41 ] proposed to learn invariant descriptors with a similarity-preserving hashing framework and a neural network to solve the underlying optimization problem. Ballesta et al [ 42 ] implemented hierarchical localization with omnidirectional images using a CNN trained to solve a classification task for distinguishing between different rooms in the environment and then a CNN trained for regression of the pose within the recognized room. Although this solution does not require converting the catadioptric images into panoramic ones, its performance is limited by the employed two-stage scheme with separated classification and regression steps.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Appearance-Based Localization with Catadioptric Cameras: Small-Footprint Models for Real-Time Inference on Edge Devices

Rostkowska

Skrzypczyński

2023

Sensors

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This paper considers the task of appearance-based localization: visual place recognition from omnidirectional images obtained from catadioptric cameras. The focus is on designing an efficient neural network architecture that accurately and reliably recognizes indoor scenes on distorted images from a catadioptric camera, even in self-similar environments with few discernible features. As the target application is the global localization of a low-cost service mobile robot, the proposed solutions are optimized toward being small-footprint models that provide real-time inference on edge devices, such as Nvidia Jetson. We compare several design choices for the neural network-based architecture of the localization system and then demonstrate that the best results are achieved with embeddings (global descriptors) yielded by exploiting transfer learning and fine tuning on a limited number of catadioptric images. We test our solutions on two small-scale datasets collected using different catadioptric cameras in the same office building. Next, we compare the performance of our system to state-of-the-art visual place recognition systems on the publicly available COLD Freiburg and Saarbrücken datasets that contain images collected under different lighting conditions. Our system compares favourably to the competitors both in terms of the accuracy of place recognition and the inference time, providing a cost- and energy-efficient means of appearance-based localization for an indoor service robot.

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A CNN Regression Approach to Mobile Robot Localization Using Omnidirectional Images

Cited by 11 publications

References 45 publications

A Survey of Machine Learning Approaches for Mobile Robot Control

A Survey of Machine Learning Approaches for Mobile Robot Control

Leveraging supplementary modalities in automated real estate valuation using comparative judgments and deep learning

Optimizing Appearance-Based Localization with Catadioptric Cameras: Small-Footprint Models for Real-Time Inference on Edge Devices

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