Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract addresses the problem of Simultaneous Localization and Mapping (SLAM) for very largeenvironments. A Hybrid architecture is presented that makes use of the Extended Kalman Filter toperform SLAM in a very eﬃcient form and a Monte Carlo localiser to resolve data association problemspotentially present when returning to a known location after a large exploration period. Algorithmsto improve the convergence of the Monte Carlo ﬁlter are presented that consider vehicle and sensoruncertainty. The proposed algorithm incorporates signiﬁcant integrity to the standard SLAM algorithmsby providing the ability to handle multimodal distributions over robot pose in real time while themapping process is on hold. Experimental results in outdoor environments are presented to demonstratethe performance of the algorithm proposed.KeywordsBayes Estimation, Bootstrap Filter, SLAM, Navigation I. IntroductionReliable autonomous navigation in highly unstructured outdoor environments presentsformidable problems in terms of sensing, perception and navigation algorithms [1]. Theproblem of localization given a map of the environment or estimating the map knowingthe vehicle position is known to be a solved problem and has been in fact applied in manyresearch and industrial applications [2] [3]. Outdoor environments present additionalchallenges due to the lack of sensors and perception algorithms that can work reliably inall environments and under all ather conditions.This is starting to change with sensors like Laser and Radars capable of returning 2-Dand 3-D reliable and consistent information and with important progress in perceptionalgorithms [4] [5]. Once the sensing and perception problem is addressed, the localizationproblem can be solved using a number of approaches. Some methods are based on thenon-linear version of the Kalman Filter, the Extended Kalman Filter (EKF). Othermethods use approximations of the probabilistic density of the states conditioned to themeasures obtained. These approaches can be classiﬁed into three categories: the mixtureof densities [6], the grid based methods [7] and the Monte Carlo methods [8].2
['Favio R. Masson', 'José E. Guivant', 'Eduardo Mario Nebot']
I am writing a research paper on a idea Robust Navigation and Mapping Architecture for Large Environments
[{"from": "human", "value": "I am writing a research paper on a idea Robust Navigation and Mapping Architecture for Large Environments"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract addresses the problem of Simultaneous Localization and Mapping (SLAM) for very largeenvironments. A Hybrid architecture is presented that makes use of the Extended Kalman Filter toperform SLAM in a very e\ufb03cient form and a Monte Carlo localiser to resolve data association problemspotentially present when returning to a known location after a large exploration period. Algorithmsto improve the convergence of the Monte Carlo \ufb01lter are presented that consider vehicle and sensoruncertainty. The proposed algorithm incorporates signi\ufb01cant integrity to the standard SLAM algorithmsby providing the ability to handle multimodal distributions over robot pose in real time while themapping process is on hold. Experimental results in outdoor environments are presented to demonstratethe performance of the algorithm proposed.KeywordsBayes Estimation, Bootstrap Filter, SLAM, Navigation I. IntroductionReliable autonomous navigation in highly unstructured outdoor environments presentsformidable problems in terms of sensing, perception and navigation algorithms [1]. Theproblem of localization given a map of the environment or estimating the map knowingthe vehicle position is known to be a solved problem and has been in fact applied in manyresearch and industrial applications [2] [3]. Outdoor environments present additionalchallenges due to the lack of sensors and perception algorithms that can work reliably inall environments and under all ather conditions.This is starting to change with sensors like Laser and Radars capable of returning 2-Dand 3-D reliable and consistent information and with important progress in perceptionalgorithms [4] [5]. Once the sensing and perception problem is addressed, the localizationproblem can be solved using a number of approaches. Some methods are based on thenon-linear version of the Kalman Filter, the Extended Kalman Filter (EKF). Othermethods use approximations of the probabilistic density of the states conditioned to themeasures obtained. These approaches can be classi\ufb01ed into three categories: the mixtureof densities [6], the grid based methods [7] and the Monte Carlo methods [8].2"}]