Dataset Information

SLAMM: Visual monocular SLAM with continuous mapping using multiple maps.

ABSTRACT: This paper presents the concept of Simultaneous Localization and Multi-Mapping (SLAMM). It is a system that ensures continuous mapping and information preservation despite failures in tracking due to corrupted frames or sensor's malfunction; making it suitable for real-world applications. It works with single or multiple robots. In a single robot scenario the algorithm generates a new map at the time of tracking failure, and later it merges maps at the event of loop closure. Similarly, maps generated from multiple robots are merged without prior knowledge of their relative poses; which makes this algorithm flexible. The system works in real time at frame-rate speed. The proposed approach was tested on the KITTI and TUM RGB-D public datasets and it showed superior results compared to the state-of-the-arts in calibrated visual monocular keyframe-based SLAM. The mean tracking time is around 22 milliseconds. The initialization is twice as fast as it is in ORB-SLAM, and the retrieved map can reach up to 90 percent more in terms of information preservation depending on tracking loss and loop closure events. For the benefit of the community, the source code along with a framework to be run with Bebop drone are made available at https://github.com/hdaoud/ORBSLAMM.

SUBMITTER: Daoud HA

PROVIDER: S-EPMC5922523 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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SLAMM: Visual monocular SLAM with continuous mapping using multiple maps.

Daoud Hayyan Afeef HA Md Sabri Aznul Qalid AQ Loo Chu Kiong CK Mansoor Ali Mohammed AM

PloS one 20180427 4

This paper presents the concept of Simultaneous Localization and Multi-Mapping (SLAMM). It is a system that ensures continuous mapping and information preservation despite failures in tracking due to corrupted frames or sensor's malfunction; making it suitable for real-world applications. It works with single or multiple robots. In a single robot scenario the algorithm generates a new map at the time of tracking failure, and later it merges maps at the event of loop closure. Similarly, maps gene ...[more]

PMID: 29702697

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Project description:Simultaneous localization and mapping (SLAM) is a fundamental problem in robotics and computer vision. It involves the task of a robot or an autonomous system navigating an unknown environment, simultaneously creating a map of the surroundings, and accurately estimating its position within that map. While significant progress has been made in SLAM over the years, challenges still need to be addressed. One prominent issue is robustness and accuracy in dynamic environments, which can cause uncertainties and errors in the estimation process. Traditional methods using temporal information to differentiate static and dynamic objects have limitations in accuracy and applicability. Nowadays, many research trends have leaned towards utilizing deep learning-based methods which leverage the capabilities to handle dynamic objects, semantic segmentation, and motion estimation, aiming to improve accuracy and adaptability in complex scenes. This article proposed an approach to enhance monocular visual odometry's robustness and precision in dynamic environments. An enhanced algorithm using the semantic segmentation algorithm DeeplabV3+ is used to identify dynamic objects in the image and then apply the motion consistency check to remove feature points belonging to dynamic objects. The remaining static feature points are then used for feature matching and pose estimation based on ORB-SLAM2 using the Technical University of Munich (TUM) dataset. Experimental results show that our method outperforms traditional visual odometry methods in accuracy and robustness, especially in dynamic environments. By eliminating the influence of moving objects, our method improves the accuracy and robustness of visual odometry in dynamic environments. Compared to the traditional ORB-SLAM2, the results show that the system significantly reduces the absolute trajectory error and the relative pose error in dynamic scenes. Our approach has significantly improved the accuracy and robustness of the SLAM system's pose estimation.

Project description:BackgroundLow-contrast letter acuity and optical coherence tomography (OCT) capture visual dysfunction and axonal loss in adult-onset multiple sclerosis (MS), and have been proposed as secondary outcome metrics for therapeutic trials. Clinical trials will soon be launched in pediatric MS, but such outcome metrics have not been well-validated in this population.ObjectivesTo determine whether MS onset during childhood and adolescence is associated with measurable loss of visual acuity and thinning of the retinal nerve fiber layer (RNFL), whether such features are noted only in the context of clinical optic nerve inflammation (optic neuritis, ON) or are a feature of MS even in the absence of optic nerve relapses, and to define the optimal methods for such detection.Study designCross-sectional study.MethodsMonocular and binocular high- and low-contrast letter acuity and contrast sensitivity were assessed in a cross-sectional cohort of children (ages 5 to 17 years) with MS (N=22 patients, 44 eyes; 8 patients with a history of ON) and disease-free controls (N=29 patients; 58 eyes) from three academic centers. Binocular summation was determined by calculating the number of letters correctly identified using the binocular score minus the better eye score for each visual test. RNFL thickness was measured using OCT (Stratus OCT-3). Results were analyzed in terms of "eyes" as: MS ON+, MS ON-, and control eyes. Generalized estimating equation (GEE) regression models were used to compare patients to controls.ResultsTraditional high-contrast visual acuity scores did not differ between MS ON+, MS ON-, and controls eyes. MS ON+ eyes had decreased monocular (p<0.001) and decreased binocular (p=0.007) low-contrast letter acuity (Sloan 1.25% contrast charts) scores. Monocular visual acuity did not differ when comparing MS ON- and control eyes. The magnitude of binocular summation using low-contrast charts was similar for pediatric MS participants and controls and was not diminished in children with a history of ON. While the mean RNFL thickness for all MS eyes (103±17 μm) trended lower when compared to corresponding measures in control eyes (109±9 μm, p=0.085), we confirmed a highly significant reduction in mean RNFL thickness in MS eyes with a history of ON (86±22 μm, p<0.001). RNFL thickness of MS ON- eyes in pediatric MS patients (109±11 μm) did not differ from controls (p=0.994).ConclusionsLow-contrast letter acuity detects subtle visual loss in MS patients with prior ON, consistent with incomplete recovery, a finding further supported by RNFL loss in ON affected eyes. In MS patients with prior unilateral ON, binocular acuity is decreased; however, the magnitude of binocular summation is preserved, unlike adult-onset MS who exhibit a reduced capacity for visual compensation in the context of unilateral injury. Also unlike findings in adult-onset MS, we did not demonstrate RNFL thinning in ON- eyes of children and adolescents with MS. Further validation is required to confirm whether neurodegeneration of visual pathways occurs in the absence of relapse, and thus whether OCT will serve as a sensitive metric for such pathology in the pediatric and adolescent MS context.

Dataset Information

SLAMM: Visual monocular SLAM with continuous mapping using multiple maps.

Publications

SLAMM: Visual monocular SLAM with continuous mapping using multiple maps.

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