Visual SLAM systems are concurrent, performance-critical systems that respond to real-time environmental conditions and are frequently deployed on resource-constrained hardware. Previous work has identified three interconnected systems challenges to building consistent, accurate, and robust SLAM systems— timeliness , concurrency , and context awareness . In this article, we analyze three popular, state-of-the-art frameworks with varying system designs and optimization techniques, and we quantify the extent to which they are affected by the aforementioned system challenges. We find that all SLAM systems must balance the interconnected nature of timeliness and accuracy, and different system designs and optimization techniques uniquely address this tension. Global-map-based SLAM systems typically achieve the best performance but suffer in resource-constrained scenarios with increased concurrency . Across all SLAM systems, incorporating context awareness into decision-making would mitigate the impact of timeliness and concurrency on accuracy in resource-constrained scenarios.

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.

Visual SLAM is a long-standing research area with many significant advances over the years. New systems typically build on previous contributions, but this requires significant development overhead, a highly detailed understanding of previous system implementations, and is rife with programming pitfalls. To enable fast experimentation and reduce the need for researchers to re-invent the wheel, we propose an extensible Visual SLAM framework with three features: modularity, seamless edge offloading, and safe concurrency.

Distributed data management systems often operate on "elastic'' clusters that can scale up or down on demand. These systems face numerous challenges, including data fragmentation, replication, and cluster sizing. Unfortunately, these challenges have traditionally been treated independently, leaving administrators with little insight on how the interplay of these decisions affects query performance. This paper introduces NashDB, an adaptive data distribution framework that relies on an economic model to automatically balance the supply and demand of data fragments, replicas, and cluster nodes. NashDB adapts its decisions to query priorities and shifting workloads, while avoiding underutilized cluster nodes and redundant replicas. This paper introduces and evaluates NashDB's model, as well as a suite of optimization techniques designed to efficiently identify data distribution schemes that match workload demands and transition the system to this new scheme with minimum data transfer overhead. Experimentally, we show that NashDB is often Pareto dominant compared to other solutions.

Data management applications deployed on IaaS cloud environments must simultaneously strive to minimize cost and provide good performance. Balancing these two goals requires complex decision-making across a number of axes: resource provisioning, query placement, and query scheduling. While previous works have addressed each axis in isolation for specific types of performance goals, this demonstration showcases WiSeDB, a cloud workload management advisor service that uses machine learning techniques to address all dimensions of the problem for customizable performance goals. In our demonstration, attendees will see WiSeDB in action for a variety of workloads and performance goals.