Robot-Beacon Distributed Range-Only SLAM for Resource-Constrained Operation

Torres-González, Arturo; Dios, J.R. Martínez-de; Ollero, A.

doi:10.3390/s17040903

Cited by 12 publications

(4 citation statements)

References 25 publications

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“…As illustrated in Figure 2, if we remove observations of a particular part in the scene, NeRF fails to model the region and tends to collapse (e.g., predicting zero density everywhere in the scene) instead of performing reasonable predictions. This poses challenges under real-world applications such as robot localization and mapping, where capturing training data can be costly, and perception of the entire scene is required [23,11,31].…”

Section: Introductionmentioning

confidence: 99%

ActiveNeRF: Learning where to See with Uncertainty Estimation

Pan¹,

Lai²,

Song³

et al. 2022

Preprint

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Recently, Neural Radiance Fields (NeRF) has shown promising performances on reconstructing 3D scenes and synthesizing novel views from a sparse set of 2D images. Albeit effective, the performance of NeRF is highly influenced by the quality of training samples. With limited posed images from the scene, NeRF fails to generalize well to novel views and may collapse to trivial solutions in unobserved regions. This makes NeRF impractical under resource-constrained scenarios. In this paper, we present a novel learning framework, ActiveNeRF, aiming to model a 3D scene with a constrained input budget. Specifically, we first incorporate uncertainty estimation into a NeRF model, which ensures robustness under few observations and provides an interpretation of how NeRF understands the scene. On this basis, we propose to supplement the existing training set with newly captured samples based on an active learning scheme. By evaluating the reduction of uncertainty given new inputs, we select the samples that bring the most information gain. In this way, the quality of novel view synthesis can be improved with minimal additional resources. Extensive experiments validate the performance of our model on both realistic and synthetic scenes, especially with scarcer training data. Code will be released at https://github.com/LeapLabTHU/ActiveNeRF.

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Section: Introductionmentioning

confidence: 99%

ActiveNeRF: Learning where to See with Uncertainty Estimation

Pan¹,

Lai²,

Song³

et al. 2022

Preprint

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“…Torres-González and colleagues proposed a scalable distributed RO-SLAM scheme that uses robot-node collaboration to provide real-time performance while improving SLAM accuracy. This algorithm was integrated in an unmanned aerial system (UAS) and evaluated in a 3D SLAM outdoor experiment [18,19].…”

Section: Introductionmentioning

confidence: 99%

A 3D Range-Only SLAM Algorithm Based on Improved Derivative UKF

et al. 2022

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In this study, we constructed a 3D range-only (RO) localization algorithm based on improved unscented Kalman filtering (UKF). The algorithm can determine the location of unknown UWB nodes in a 3D environment through a moving node with low computational complexity, which can help agents to accurately identify feature points in 3D SLAM based only on the range. Specifically, we established an original UKF framework based the 3D RO localization algorithm, and developed a derivative UKF framework to reduce the computational complexity of the algorithm. We used singular value decomposition to compensate for the robustness of the algorithm. Next, we performed a theoretical analysis to show that our method reduces the computational burden without reducing the stability or accuracy of the system. Finally, we conducted numerical simulations and physical experiments to show the effectiveness of the developed 3D RO localization algorithm.

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“…Except for the traditional direct robot-beacon measurements, additional inter-beacon measurements are also integrated into the SEIF, which reduces uncertainty both in the map estimation and localization accuracy. This work was then extended to a 3D resource-constrained operation with an auxiliary selection tool which only integrates the most informative measurements into the SEIF [36]. Further analysis regarding the effect of inter-beacon measurements on the building of an information matrix followed in [37], validating the preservation of the main properties of SEIF.…”

Section: Introductionmentioning

confidence: 99%

“…The shortcomings of these two methods resulted in them being quickly replaced by another widely used delayed initialization method: particle filters (PFs) [23,36,37,42,43]. PFs model the probability density of the beacon location as a uniform distribution around the measurement circle, and then the weight of each particle is updated and finally converges to a single solution by accumulating successive range measurements.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Radio Beacon/IMU/Altimeter Integrated Localization Scheme with Uncertain Initial Beacon Locations for Lunar Pinpoint Landing

Luo³

et al. 2020

Sensors

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As a growing number of exploration missions have successfully landed on the Moon in recent decades, ground infrastructures, such as radio beacons, have attracted a great deal of attention in the design of navigation systems. None of the available studies regarding integrating beacon measurements for pinpoint landing have considered uncertain initial beacon locations, which are quite common in practice. In this paper, we propose a radio beacon/inertial measurement unit (IMU)/altimeter localization scheme that is sufficiently robust regarding uncertain initial beacon locations. This scheme was designed based on the sparse extended information filter (SEIF) to locate the lander and update the beacon configuration at the same time. Then, an adaptive iterated sparse extended hybrid filter (AISEHF) was devised by modifying the prediction and update stage of SEIF with a hybrid-form propagation and a damping iteration algorithm, respectively. The simulation results indicated that the proposed method effectively reduced the error in the position estimations caused by uncertain beacon locations and made an effective trade-off between the estimation accuracy and the computational efficiency. Thus, this method is a potential candidate for future lunar exploration activities.

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Robot-Beacon Distributed Range-Only SLAM for Resource-Constrained Operation

Cited by 12 publications

References 25 publications

ActiveNeRF: Learning where to See with Uncertainty Estimation

ActiveNeRF: Learning where to See with Uncertainty Estimation

A 3D Range-Only SLAM Algorithm Based on Improved Derivative UKF

A Distributed Radio Beacon/IMU/Altimeter Integrated Localization Scheme with Uncertain Initial Beacon Locations for Lunar Pinpoint Landing

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