Localization in urban environments is becoming increasingly important and used in tools such as ARCore [ 18 ], ARKit [ 34 ] and others. One popular mechanism to achieve accurate indoor localization and a map of the space is using Visual Simultaneous Localization and Mapping (Visual-SLAM). However, Visual-SLAM is known to be resource-intensive in memory and processing time. Furthermore, some of the operations grow in complexity over time, making it challenging to run on mobile devices continuously. Edge computing provides additional compute and memory resources to mobile devices to allow offloading tasks without the large latencies seen when offloading to the cloud. In this article, we present Edge-SLAM, a system that uses edge computing resources to offload parts of Visual-SLAM. We use ORB-SLAM2 [ 50 ] as a prototypical Visual-SLAM system and modify it to a split architecture between the edge and the mobile device. We keep the tracking computation on the mobile device and move the rest of the computation, i.e., local mapping and loop closing, to the edge. We describe the design choices in this effort and implement them in our prototype. Our results show that our split architecture can allow the functioning of the Visual-SLAM system long-term with limited resources without affecting the accuracy of operation. It also keeps the computation and memory cost on the mobile device constant, which would allow for the deployment of other end applications that use Visual-SLAM. We perform a detailed performance and resources use (CPU, memory, network, and power) analysis to fully understand the effect of our proposed split architecture.

Many existing SLAM approaches rely on the assumption of static environments for accurate performance. However, several robot applications require them to traverse repeatedly in semi-static or dynamic environments. There has been some recent research interest in designing persistence filters to reason about persistence in such scenarios. Our goal in this work is to incorporate such persistence reasoning in visual SLAM. To this end, we incorporate persistence filters [1] into ORB-SLAM, a well-known visual SLAM algorithm. We observe that the simple integration of their proposal results in inefficient persistence reasoning. Through a series of modifications and using two locally collected datasets, we demonstrate the utility of such persistence filtering as well as our customizations in ORB-SLAM. Overall, incorporating persistence filtering could result in a significant reduction in map size (about 30% in the best case) and a corresponding reduction in run-time while retaining similar accuracy to methods that use much larger maps.

The recent advances in mobile devices have allowed them to run spatial sensing algorithms such as Visual Simultaneous Localization and Mapping (Visual-SLAM). However, the resource requirements of Visual-SLAM prevents long-operation of such algorithm on mobile devices. We demonstrate Edge-SLAM [2], a system that adapts edge computing into Visual-SLAM through a split architecture. Edge-SLAM offloads the compute-intensive modules of Visual-SLAM to the edge without losing accuracy. Our experiments show that Edge-SLAM architecture reduces the use of computation and memory resources on mobile devices and keeps it constant. Thus, enabling long-operation of Visual-SLAM along with other applications services on mobile devices.

Localization in urban environments is becoming increasingly important and used in tools such as ARCore [11], ARKit [27] and others. One popular mechanism to achieve accurate indoor localization as well as a map of the space is using Visual Simultaneous Localization and Mapping (Visual-SLAM). However, Visual-SLAM is known to be resource-intensive in memory and processing time. Further, some of the operations grow in complexity over time, making it challenging to run on mobile devices continuously. Edge computing provides additional compute and memory resources to mobile devices to allow offloading of some tasks without the large latencies seen when offloading to the cloud. In this paper, we present Edge-SLAM, a system that uses edge computing resources to offload parts of Visual-SLAM. We use ORB-SLAM2 as a prototypical Visual-SLAM system and modify it to a split architecture between the edge and the mobile device. We keep the tracking computation on the mobile device and move the rest of the computation, i.e., local mapping and loop closure, to the edge. We describe the design choices in this effort and implement them in our prototype. Our results show that our split architecture can allow the functioning of the Visual-SLAM system long-term with limited resources without affecting the accuracy of operation. It also keeps the computation and memory cost on the mobile device constant which would allow for deployment of other end applications that use Visual-SLAM.

Simultaneous Localization and Mapping (SLAM) is the process of learning about both the environment and about a robot's location with respect to the environment and is essential for robots to autonomously navigate. A variety of algorithms using many different sensors such as RGB-D cameras, laser range finders, ultrasonic sensors and others have been proposed to perform SLAM. However, these algorithms face common challenges are that of computational complexity, wrong loop closure detection and failure to localize correctly when robot loses state (kidnapped robot problem). In this work, we utilize Wi-Fi signal strength sensing to aid the SLAM process in indoor environments and address the challenges mentioned above.

This paper addresses the problem of recovering sparse link vectors with network topological constraints that is motivated by network inference and tomography applications. We propose a novel framework called UCS-NT in the context of compressive sensing for sparse recovery in networks. In order to efficiently recover sparse specification of link vectors, we construct a feasible measurement matrix using this framework through connected paths. It is theoretically shown that, only O(k log(n)) path measurements are sufficient for uniquely recovering any k-sparse link vector. Moreover, extensive simulations demonstrate that this framework would converge to an accurate solution for a wide class of networks.