A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks

Huang, Baichuan; Zhao, Jun; Liu, Jingbin

doi:10.48550/arxiv.1909.05214

Cited by 24 publications

(32 citation statements)

References 160 publications

(168 reference statements)

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“…Robot-edge communication. The communications among robots, edge servers, and the coordinator are conducted via ROS 2 . They all communicate using the publisher-subscriber model.…”

Section: Methodsmentioning

confidence: 99%

Edge Robotics: Edge-Computing-Accelerated Multi-Robot Simultaneous Localization and Mapping

Huang¹,

Zeng²,

Xu³

et al. 2021

Preprint

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With the wide penetration of smart robots in multifarious fields, Simultaneous Localization and Mapping (SLAM) technique in robotics has attracted growing attention in the community. Yet collaborating SLAM over multiple robots still remains challenging due to performance contradiction between the intensive graphics computation of SLAM and the limited computing capability of robots. While traditional solutions resort to the powerful cloud servers acting as an external computation provider, we show by real-world measurements that the significant communication overhead in data offloading prevents its practicability to real deployment. To tackle these challenges, this paper promotes the emerging edge computing paradigm into multi-robot SLAM and proposes RecSLAM, a multi-robot laser SLAM system that focuses on accelerating map construction process under the robot-edge-cloud architecture. In contrast to conventional multi-robot SLAM that generates graphic maps on robots and completely merges them on the cloud, RecSLAM develops a hierarchical map fusion technique that directs robots' raw data to edge servers for real-time fusion and then sends to the cloud for global merging. To optimize the overall pipeline, an efficient multi-robot SLAM collaborative processing framework is introduced to adaptively optimize robot-to-edge offloading tailored to heterogeneous edge resource conditions, meanwhile ensuring the workload balancing among the edge servers. Extensive evaluations show RecSLAM can achieve up to 39.31% processing latency reduction over the state-of-the-art. Besides, a proof-of-concept prototype is developed and deployed in real scenes to demonstrate its effectiveness.

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“…Robot-edge communication. The communications among robots, edge servers, and the coordinator are conducted via ROS 2 . They all communicate using the publisher-subscriber model.…”

Section: Methodsmentioning

confidence: 99%

Edge Robotics: Edge-Computing-Accelerated Multi-Robot Simultaneous Localization and Mapping

Huang¹,

Zeng²,

Xu³

et al. 2021

Preprint

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“…Another area of work that considers object localization is SLAM [5], [6]. The traditional goal of SLAM is to provide localization for a robot by constructing a map of an unknown environment and localizing the robot in that map [29].…”

Section: B Related Workmentioning

confidence: 99%

“…While there is a large amount of research on localization and detection algorithms in the field of robotics, majority of these algorithms are simplified and do not consider the practical side of fielding real sensors on multiple robots such as applying real detection algorithms to their observations. For example, a basic SLAM (simultaneous localization and mapping) formulation focuses on the uncertainty of locations while abstracting the detection of objects [5], [6]. Similarly, common coverage planners produce only simplistic plans with an abstraction on the object detectors [7].…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Active Search using Realistic Depth-Aware Noise Model

Ghods

Durkin

Schneider

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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The search for objects of interest in an unknown environment by making data-collection decisions (i.e., active search or active sensing) has robotics applications in many fields, including the search and rescue of human survivors following disasters, detecting gas leaks or locating and preventing animal poachers. Existing algorithms often prioritize the location accuracy of objects of interest while other practical issues such as the reliability of object detection as a function of distance and lines of sight remain largely ignored. An additional challenge is that in many active search scenarios, communication infrastructure may be damaged, unreliable, or unestablished, making centralized control of multiple search agents impractical. We present an algorithm called Noise-Aware Thompson Sampling (NATS) that addresses these issues for multiple ground-based robot agents performing active search considering two sources of sensory information from monocular optical imagery and sonar tracking. NATS utilizes communications between robot agents in a decentralized manner that is robust to intermittent loss of communication links. Additionally, it takes into account object detection uncertainty from depth as well as environmental occlusions. Using simulation results, we show that NATS significantly outperforms existing methods such as information-greedy policies or exhaustive search. We demonstrate the real-world viability of NATS using a photorealistic environment created in the Unreal Engine 4 game development platform with the AirSim plugin.

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“…In robotics research, methods that deal with active search generally aim at autonomously building topological (identify obstacles and clearways) and/or spatial maps of a region. Our active search problem differs from topological mapping techniques such as SLAM [28,29] and can be most closely related to spatial mapping. For example, [2] identifies strong signals in environments with background information using trajectory planning with confidence intervals; but, unlike our problem setting, their algorithm is developed for a single agent performing point sensing observations.…”

Section: Related Workmentioning

confidence: 99%

Asynchronous Multi Agent Active Search

Ghods,

Banerjee,

Schneider

2020

Preprint

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Active search refers to the problem of efficiently locating targets in an unknown environment by actively making data-collection decisions, and has many applications including detecting gas leaks, radiation sources or human survivors of disasters using aerial and/or ground robots (agents). Existing active search methods are in general only amenable to a single agent, or if they extend to multi agent they require a central control system to coordinate the actions of all agents. However, such control systems are often impractical in robotics applications. In this paper, we propose two distinct active search algorithms called SPATS (Sparse Parallel Asynchronous Thompson Sampling) and LATSI (LAplace Thompson Sampling with Information gain) that allow for multiple agents to independently make datacollection decisions without a central coordinator. Throughout we consider that targets are sparsely located around the environment in keeping with compressive sensing assumptions and its applicability in real world scenarios. Additionally, while most common search algorithms assume that agents can sense the entire environment (e.g. compressive sensing) or sense point-wise (e.g. Bayesian Optimization) at all times, we make a realistic assumption that each agent can only sense a contiguous region of space at a time. We provide simulation results as well as theoretical analysis to demonstrate the efficacy of our proposed algorithms.Preprint. Under review.

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A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks

Cited by 24 publications

References 160 publications

Edge Robotics: Edge-Computing-Accelerated Multi-Robot Simultaneous Localization and Mapping

Edge Robotics: Edge-Computing-Accelerated Multi-Robot Simultaneous Localization and Mapping

Multi-Agent Active Search using Realistic Depth-Aware Noise Model

Asynchronous Multi Agent Active Search

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