Improving visual SLAM accuracy through deliberate camera oscillations

Heshmat, Mohamed; Abdellatif, Mohamed; Abbas, Hossam S.

doi:10.1109/rose.2013.6698435

Cited by 4 publications

(7 citation statements)

References 31 publications

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“…Perceptual Aliasing: (Heshmat et al, 2013) incorporates a hardware solution to enhance feature measurements and localization by adding lateral motion to the camera. (Codd-Downey and Jenkin, 2017;Garca et al, 2016) combine information from multiple sensors for increasing localization and mapping accuracy.…”

Section: Related Workmentioning

confidence: 99%

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: Related Workmentioning

confidence: 99%

Augmenting visual SLAM with Wi-Fi sensing for indoor applications

et al. 2019

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“…To solve camera observability problem, we are using a camera with superimposed oscillations, which was shown to improve depth estimates of the features [9]. It is known that during the human walk the head moves in a horizontal plane from side to side [7,13].…”

Section: Introductionmentioning

confidence: 99%

“…Camera oscillations are superimposed on the camera main motion (robot motion) to improve features depth estimation [9]. Besides the improvement of the depth estimation of the points, this biologically inspired idea solves the monocular camera observability problem.…”

Section: Introductionmentioning

confidence: 99%

Dynamic feature detection using virtual correction and camera oscillations

Heshmat

Abdellatif

Nakamura

et al. 2014

2014 International Conference on 3D Imaging (IC3D)

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Visual SLAM algorithms exploit natural scene features to infer the camera motion and build a map of a static environment. In this paper, we relax the severe assumption of a static scene to allow for the detection and deletion of dynamic points. A new "virtual correction" method is introduced which serves to detect the dynamic points by checking the re-projection error of the points before and after the virtual measurement update. It can also recover the erroneously excluded useful features, particularly the distant points which may be deleted because of the change in its position after new measurement observation. Deliberate camera oscillations are also used to improve the VSLAM accuracy and the camera observability. The simulation results showed the effectiveness of the virtual correction in improving the consistency of the VSLAM and the detection of dynamic points and in particular for difficult scenarios.

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“…enough physical clues to get the landmarks distances, especially, in the forward and curved robot motion [15]. Camera oscillation was proposed as a solution for the lower physical clues case like the forward and the curved robot motion [5]. This can be regarded as an improvement in camera geometry for a time stereo, since we do not control main motion (robot motion), but we can control a secondary motion (camera oscillations).…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to the current direction of image stabilization or anti-shaking camera systems, camera oscillations showed better performance for the VSLAM and improved the features convergence and the robot localization. Camera oscillation means to superimpose oscillations on the camera / robot main motion, in a lateral direction to give sufficient physical clues for the solution [5]. This idea stems from the well-known fact that human and several animals use oscillation to improve the distance estimation.…”

Section: Introductionmentioning

confidence: 99%

Camera oscillation pattern for VSLAM: Translational versus rotational

Heshmat

Abdellatif

Nakamura

et al. 2014

2014 International Conference on 3D Imaging (IC3D)

Self Cite

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Visual SLAM algorithms exploit natural scene features to infer the camera motion and build a map of the environment landmarks. SLAM algorithm has two interrelated processes localization and mapping. For accurate localization, we need the features location estimates to converge quickly. On the other hand, to build an accurate map, we need accurate localization. Recently, a biologically inspired approach exploits deliberate camera oscillation to improve the convergence speed of depth estimate. In this paper, we explore the effect of camera oscillation pattern on the accuracy of VSLAM. Two main oscillation patterns are used for distance estimation: translational and rotational. Experiments, using static and moving robot, are made to explore the effect of these oscillation patterns on the VSLAM performance.

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Improving visual SLAM accuracy through deliberate camera oscillations

Cited by 4 publications

References 31 publications

Augmenting visual SLAM with Wi-Fi sensing for indoor applications

Augmenting visual SLAM with Wi-Fi sensing for indoor applications

Dynamic feature detection using virtual correction and camera oscillations

Camera oscillation pattern for VSLAM: Translational versus rotational

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