Modeling Perceptual Aliasing in SLAM via Discrete-Continuous Graphical Models

Perceptual aliasing is one of the main causes of failure for Simultaneous Localization and Mapping (SLAM) systems operating in the wild. Perceptual aliasing is the phenomenon where different places generate a similar visual (or, in general, perceptual) footprint. This causes spurious measurements to be fed to the SLAM estimator, which typically results in incorrect localization and mapping results. The problem is exacerbated by the fact that those outliers are highly correlated, in the sense that perceptual aliasing creates a large number of mutually-consistent outliers. Another issue stems from the fact that most state-of-the-art techniques rely on a given trajectory guess (e.g., from odometry) to discern between inliers and outliers and this makes the resulting pipeline brittle, since the accumulation of error may result in incorrect choices and recovery from failures is far from trivial. This work provides a unified framework to model perceptual aliasing in SLAM and provides practical algorithms that can cope with outliers without relying on any initial guess. We present two main contributions. The first is a Discrete-Continuous Graphical Model (DC-GM) for SLAM: the continuous portion of the DC-GM captures the standard SLAM problem, while the discrete portion describes the selection of the outliers and models their correlation. The second contribution is a semidefinite relaxation to perform inference in the DC-GM that returns estimates with provable sub-optimality guarantees. Experimental results on standard benchmarking datasets show that the proposed technique compares favorably with state-of-the-art methods while not relying on an initial guess for optimization.Comment: 13 pages, 14 figures, 1 tabl

D2D multi-hop routing : collision probability and routing strategy with limited location information

In this paper, we define a collision area in a heterogeneous cellular network for the purpose of interference management between Device-to-Device (D2D) and conventional cellular (CC) communications. Currently, most D2D routing algorithms assume synchronized accurate location knowledge among users and the base stations. In reality, this level of location accuracy is difficult and power consuming in Universal Mobile Telecommunications System (UMTS). In current LongTerm Evolution (LTE), there is no location information from the cell besides range information from time measurements. In the absence of accurate location information, we analyze the collision probability of the D2D multi-hop path hitting the defined collision area. Specifically, we consider the problem for three different routing scenarios: intra-cell, intra-cell to cell boundary, and cell boundary to boundary routing. As a result, we propose a dynamic switching strategy between D2D and CC communications in order to minimize mutual interference. The gradient-based switching strategy can avoid collision with the collision area and only requires knowledge of the current user and the final destination user’s distances to the serving base station

Device-to-device communications in LTE-unlicensed heterogeneous Network

In this article, we look into how the LTE network can efficiently evolve to cater for new data services by utilizing direct communications between mobile devices and extending the direct transmissions to the unlicensed bands, that is, D2D communications in conjunction with LTE-Unlicensed. In doing so, it provides an opportunity to solve the main challenge of mutual interference between D2D and CC transmissions. In this context, we review three interconnected major technical areas of multihop D2D: transmission band selection, routing path selection, and resource management. Traditionally, D2D transmissions are limited to specific regions of a cell's coverage area in order to limit the interference to CC primary links. We show that by allowing D2D to operate in the unlicensed bands with protective fairness measures for WiFi transmissions, D2D is able to operate across the whole coverage area and, in doing so, efficiently scale the overall network capacity while minimizing cross-tier and cross-technology interference

Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL

Cooperative multi-agent reinforcement learning (MARL) is making rapid progress for solving tasks in a grid world and real-world scenarios, in which agents are given different attributes and goals, resulting in different behavior through the whole multi-agent task. In this study, we quantify the agent's behavior difference and build its relationship with the policy performance via {\bf Role Diversity}, a metric to measure the characteristics of MARL tasks. We define role diversity from three perspectives: action-based, trajectory-based, and contribution-based to fully measure a multi-agent task. Through theoretical analysis, we find that the error bound in MARL can be decomposed into three parts that have a strong relation to the role diversity. The decomposed factors can significantly impact policy optimization on three popular directions including parameter sharing, communication mechanism, and credit assignment. The main experimental platforms are based on {\bf Multiagent Particle Environment (MPE)} and {\bf The StarCraft Multi-Agent Challenge (SMAC). Extensive experiments} clearly show that role diversity can serve as a robust measurement for the characteristics of a multi-agent cooperation task and help diagnose whether the policy fits the current multi-agent system for a better policy performance

Interference-aware multi-hop path selection for device-to-device communications in a cellular interference environment

Device-to-Device (D2D) communications is widely seen as an efficient network capacity scaling technology. The co-existence of D2D with conventional cellular (CC) transmissions causes unwanted interference. Existing techniques have focused on improving the throughput of D2D communications by optimising the radio resource management and power allocation. However, very little is understood about the impact of the route selection of the users and how optimal routing can reduce interference and improve the overall network capacity. In fact, traditional wisdom indicates that minimising the number of hops or the total path distance is preferable. Yet, when interference is considered, we show that this is not the case. In this paper, we show that by understanding the location of the user, an interference-aware routing algorithm can be devised. We propose an adaptive Interference-Aware-Routing (IAR) algorithm, that on average achieves a 30% increase in hop distance, but can improve the overall network capacity by 50% whilst only incurring a minor 2% degradation to the CC capacity. The analysis framework and the results open up new avenues of research in location-dependent optimization in wireless systems, which is particularly important for increasingly dense and semantic-aware deployments

Maximum Entropy Heterogeneous-Agent Mirror Learning

Multi-agent reinforcement learning (MARL) has been shown effective for cooperative games in recent years. However, existing state-of-the-art methods face challenges related to sample inefficiency, brittleness regarding hyperparameters, and the risk of converging to a suboptimal Nash Equilibrium. To resolve these issues, in this paper, we propose a novel theoretical framework, named Maximum Entropy Heterogeneous-Agent Mirror Learning (MEHAML), that leverages the maximum entropy principle to design maximum entropy MARL actor-critic algorithms. We prove that algorithms derived from the MEHAML framework enjoy the desired properties of the monotonic improvement of the joint maximum entropy objective and the convergence to quantal response equilibrium (QRE). The practicality of MEHAML is demonstrated by developing a MEHAML extension of the widely used RL algorithm, HASAC (for soft actor-critic), which shows significant improvements in exploration and robustness on three challenging benchmarks: Multi-Agent MuJoCo, StarCraftII, and Google Research Football. Our results show that HASAC outperforms strong baseline methods such as HATD3, HAPPO, QMIX, and MAPPO, thereby establishing the new state of the art. See our project page at https://sites.google.com/view/mehaml