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A static world assumption is often used when considering the simultaneous localization and mapping (SLAM) problem. In reality, especially when long-term autonomy is the objective, this is not a valid assumption. This paper studies a scenario where landmarks can occupy multiple discrete positions at different points in time, where each possible position is added to a multi-hypothesis map representation. A selector-mixture distribution is introduced and used in the observation model. Each landmark position hypothesis is associated with one component in the mixture. The landmark movements are modeled by a discrete Markov chain and the Monte Carlo tree search algorithm is suggested to be used as component selector. The non-static environment model is further incorporated into the factor graph formulation of the SLAM problem and is solved by iterating between estimating discrete variables with a component selector and optimizing continuous variables with an efficient state-of-the-art nonlinear least squares SLAM solver. The proposed non-static SLAM system is validated in numerical simulation and with a publicly available dataset by showing that a non-static environment can successfully be navigated. [ABSTRACT FROM AUTHOR]

Compared with traditional video surveillance systems, wireless video sensor systems are more suitable for emergency application scenarios, such as underground coal mine disaster rescue, due to their low power consumption and rapid deployment. Considering the limited computing power and transmission bandwidth of video sensor nodes, we propose an adaptive compression and hybrid multiple hypothesis based residual reconstruction algorithm based on normalized Bhattacharyya coefficient (NBCAC-MHRR) to solve the high efficiency video coding (HEVC) problem in underground coal mines. First, a low-complexity adaptive sampling rate allocation method is performed on the encoding side. Second, by integrating the background subtraction idea, we combine the high-quality reconstruction of the foreground with the multi-hypothesis residual reconstruction of the background to improve the overall reconstruction effect of the video sequence. Simulation results show that the proposed algorithm can achieve higher reconstruction quality and efficiency than previous methods, especially in underground coal mine application scenarios.

• We present a new Transformer-based method, called Multi-Hypothesis Transformer (MHFormer++), for 3D human pose estimation from monocular videos. It builds a one-to-many-to-one framework, which can effectively learn spatiotemporal representations of multiple pose hypotheses in an end-to-end manner. • A Multi-Hypothesis Generation (MHG) module is designed to capture both global and local information of human body joints within each frame and generate multiple hypothesis representations containing diverse semantic information in the spatial domain • A Self-Hypothesis Refinement (SHR) module and a Cross-Hypothesis Interaction (CHI) module are introduced to model temporal consistencies across frames and communicate among multi-hypothesis features both independently and mutually in the temporal domain • The proposed method achieves state-of-the-art performance on two challenging 3D human pose estimation benchmark datasets. Highlights (for review) Despite significant progress, estimating 3D human poses from monocular videos remains a challenging task due to depth ambiguity and self-occlusion. Most existing works attempt to solve both issues by exploiting spatial and temporal relationships. However, those works ignore the fact that it is an inverse problem where multiple feasible solutions (i.e. , hypotheses) exist. To relieve this limitation, we propose a Multi-Hypothesis Transformer that learns spatio-temporal representations of multiple plausible pose hypotheses. In order to effectively model multi-hypothesis dependencies and build strong relationships across hypothesis features, we introduce a one-to-many-to-one three-stage framework: (i) Generate multiple initial hypothesis representations; (ii) Model self-hypothesis communication, merge multiple hypotheses into a single converged representation and then partition it into several diverged hypotheses; (iii) Learn cross-hypothesis communication and aggregate the multi-hypothesis features to synthesize the final 3D pose. Through the above processes, the final representation is enhanced and the synthesized pose is much more accurate. Extensive experiments show that the proposed method achieves state-of-the-art results on two challenging datasets: Human3.6M and MPI-INF-3DHP. The code and models are available at https://github.com/Vegetebird/MHFormer. [ABSTRACT FROM AUTHOR]

In this paper, a multihypothesis-based centralized Failure Diagnosis in Protection System (FDProSys) tool using phasor measurement units (PMU) data has been proposed to systematically and automatically detect malfunctions or failures in protective devices. The proposed diagnostic algorithm and FDProSys tool employs synchrophasor measurements to select a section of the network called ProNet, with possible device malfunction. Multiple hypotheses have been generated automatically based on node incidence matrices and multihypothesis theory. The malfunction of the protective devices in the presence of fault and relay operation is identified by comparing the five-digit message of the real event with multiple hypotheses. Trustworthiness of PMU data caused by common failures, such as communication outage and GPS problems, has also been considered by the proposed algorithm using the PMU status flag. The developed tool FDProSys can also work with supervisory control and data acquisition data assisted by manual analysis with less accuracy and is robust enough to several PMU/relay failures. Simulation results using the IEEE 14-bus, IEEE 57-bus, and IEEE 300-bus systems demonstrate the accuracy of the developed algorithm. [ABSTRACT FROM AUTHOR]

This paper describes the Lagrange based formulation of bit-assignment for multi-hypothesis frames over number of pictures in a group for motion compensated video compression. Theoretical insight on bit assignment is accomplished by applying Lagrange multiplier and its cost function for a real-time computational analysis. Video signals are compressed at optimal bit allocation for qualitative reconstruction at the decoder (receiver) depending upon the number of hypothesis used in coding the signal. This has made multi-hypothesis motion compensation (MHMC) a great process in the prediction of the actual signal through the combination of more than one motion compensated prediction (MCP). Optimal Bits were allocated to multi-hypothesis frames using MATLAB to optimize the number of hypothesis frames per group of pictures (GOP) decoded for an improved signal compression. Simulation results show that an optimal bit assignment of the range 0.089 bpp to 0.159 bpp was realized with eight numbers of hypotheses frames per (GOP). This implies that varying number of hypotheses actualizes optimal bit assignment for motion compensation. This shows a qualitative signal compression performance analysis for high technology motion pictures in communication services and storage processes. It also addresses a better prediction due to greater time correlation between hypotheses and helps in the improvement of coding and reconstruction quality of video signals. [ABSTRACT FROM AUTHOR]

In theory, multi-hypothesis motion compensation prediction (MHMCP) can enhance the prediction quality of motion compensation prediction. Traditional MHMCP methods use fixed weightings for the linear combination of the multiple signal sources which may not be optimum. Moreover, MHMCP requires the transmission of more than one motion vector, which will increase the side information to be transmitted. We discovered that using estimated distortion ratio, a weighting pair can be estimated adaptively for the linear combination of two signal blocks to form a prediction block with a lower distortion. The proposed MCP method does not require the transmission of additional side information yet has a better prediction accuracy than conventional motion compensation prediction. In addition, the proposed method has relatively low algorithmic decision overhead. It can be implemented in hardware easily to support the realization of real time high-quality video coding. [ABSTRACT FROM AUTHOR]

Many robust state-of-the-art localization methods rely on pose-space sample sets that are evaluated against individual sensor measurements. While these methods can work effectively, they often provide limited mechanisms to control the amount of hypotheses based on their similarity. Furthermore, they do not explicitly use associations to create or remove these hypotheses. We propose a global localization strategy that allows a mobile robot to localize using explicit symbolic associations with annotated geometric features. The feature measurements are first combined locally to form a consistent local feature map that is accurate in the vicinity of the robot. Based on this local map, an association tree is maintained that pairs local map features with global map features. The leaves of the tree represent distinct hypotheses on the data associations that allow for globally unmapped features appearing in the local map. We propose a registration step to check if an association hypothesis is supported. Our implementation considers a robot equipped with a 2D LiDAR and we compare the proposed method to a particle filter. We show that maintaining a smaller set of data association hypotheses results in better performance and explainability of the robot's assumptions, as well as allowing more control over hypothesis bookkeeping. We provide experimental evaluations with a physical robot in a real environment using an annotated geometric building model that contains only the static part of the indoor scene. The result shows that our method outperforms a particle filter implementation in most cases by using fewer hypotheses with more descriptive power., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Hendrikx, Bruyninckx, Elfring and Van De Molengraft.)