Rorg: Service Robot Software Management with Linux Containers

Abstract

Scaling up the software system on service robots significantly increases the maintenance burden of developers and the risk of resource contention of the computer embedded with robots. As a result, developers spend much time on configuring, deploying, and monitoring the robot software system; robots utilize significant computer resources when all software processes are running. We propose Rorg, a Linux container-based scheme to manage, schedule, and monitor software components on service robots. Although Linux container is already widely-used in cloud environments, this technique is challenging to efficiently adopt in service robot systems due to the unique characteristics of service robots — multi-purpose systems with resource limitations and high performance requirements. To pave the way of Linux containers on service robots in an efficient manner, we propose a programmable container management interface and a resource time-sharing mechanism incorporated with the robot operating system (ROS). Rorg allows the developers to pack software into self-contained images and runs them in isolated environments using Linux containers; it also allows the robot to turn on and off software components on demand to avoid resource contention. We evaluate Rorg with a long-term autonomous tour guide robot: It manages 41 software components on the robot and relieved our maintenance burden, and it also reduces CPU by 45.5% and memory usage by 16.5% on average.
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OmniMapper: A Modular Multimodal Mapping Framework

Abstract

Simultaneous Localization and Mapping (SLAM) is not a problem with a one-size-fits-all solution. The literature includes a variety of SLAM approaches targeted at different environments, platforms, sensors, CPU budgets, and applications. We propose OmniMapper, a modular multimodal framework and toolbox for solving SLAM problems. The system can be used to generate pose graphs, do feature-based SLAM, and also includes tools for semantic mapping. Multiple measurement types from different sensors can be combined for multimodal mapping. It is open with standard interfaces to allow easy integration of new sensors and feature types. We present a detailed description of the mapping approach, as well as a software framework that implements this, and present detailed descriptions of its applications to several domains including mapping with a service robot in an indoor environment, large- scale mapping on a PackBot, and mapping with a handheld RGBD camera.
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Planar Surface SLAM with 3D and 2D Sensors

Abstract

We present an extension to our feature based mapping technique that allows for the use of planar surfaces such as walls, tables, counters, or other planar surfaces as landmarks in our mapper. These planar surfaces are measured both in 3D point clouds, as well as 2D laser scans. These sensing modalities compliment each other well, as they differ significantly in their measurable fields of view and maximum ranges. We present experiments to evaluate the contributions of each type of sensor.
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