ros2_tracing: Multipurpose Low-Overhead Framework for Real-Time Tracing of ROS 2

Bédard, Christophe; Lütkebohle, Ingo; Dagenais, Michel R.

doi:10.1109/lra.2022.3174346

Cited by 27 publications

(11 citation statements)

References 14 publications

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“…Following this methodology, in our case study we begin by instrumenting both ROS 2 and our target kernels with LTTng probes. Reusing past work and probes [25] allows us to easily get a grasp of the dataflow interactions within rmw, rcl, and rclcpp ROS 2 layers. We then also instrument the ResizeNode and RectifyNode components of the image pipeline package used in our case study.…”

Section: A Methodologymentioning

confidence: 99%

“…In order to trace and evaluate the relative performance of both ROS 2 individual Nodes and complete computational graphs, we leverage Linux Tracing Toolkit next generation (LTTng [26]) for tracing and benchmarking. Building upon prior work [25], LTTng provides a collection of flexible tracing tools and multipurpose instrumentation for ROS 2 that allow collecting runtime execution information in realtime in distributed systems using low-overhead tracers. Instrumentation probes introduced in ROS 2 Nodes can be enabled or disabled at build-time.…”

Section: Low-overhead Real-time Tracing and Benchmarking -4mentioning

confidence: 99%

“…Instrumentation probes introduced in ROS 2 Nodes can be enabled or disabled at build-time. When enabling all ROS 2 instrumentation, end-to-end message latency overhead is below 5.5us [25], making it suitable for a wide variety of hardware acceleration use cases. This infrastructure also lays a foundation for future integration with platform-specific performance counters and tracing tools that can extend analysis to more fine-grained introspection and profiling of the kernels running onboard an accelerator device.…”

Section: Low-overhead Real-time Tracing and Benchmarking -4mentioning

confidence: 99%

“…Full end-to-end system analysis is required to understand how different implementation tradeoffs impact overall performance. To enable this analysis, we demonstrate how to leverage prior work [25] and benchmark our ROS hardware acceleration framework with a low-overhead framework for real-time tracing based on the Linux Tracing Toolkit next generation (LTTng) [26]. We demonstrate analysis of a case study deployment using CPU and FPGA nodes for a simple perception pipeline.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

RobotCore: An Open Architecture for Hardware Acceleration in ROS 2

Mayoral-Vilches¹,

Neuman²,

Plancher³

et al. 2022

Preprint

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Hardware acceleration can revolutionize robotics, enabling new applications by speeding up robot response times while remaining power-efficient. However, the diversity of acceleration options makes it difficult for roboticists to easily deploy accelerated systems without expertise in each specific hardware platform. In this work, we address this challenge with RobotCore, an architecture to integrate hardware acceleration in the widely-used ROS 2 robotics software framework. This architecture is target-agnostic (supports edge, workstation, data center, or cloud targets) and accelerator-agnostic (supports both FPGAs and GPUs). It builds on top of the common ROS 2 build system and tools and is easily portable across different research and commercial solutions through a new firmware layer. We also leverage the Linux Tracing Toolkit next generation (LTTng) for low-overhead real-time tracing and benchmarking. To demonstrate the acceleration enabled by this architecture, we use it to deploy a ROS 2 perception computational graph on a CPU and FPGA.We employ our integrated tracing and benchmarking to analyze bottlenecks, uncovering insights that guide us to improve FPGA communication efficiency. In particular, we design an intra-FPGA ROS 2 node communication queue to enable faster data flows, and use it in conjunction with FPGA-accelerated nodes to achieve a 24.42% speedup over a CPU.

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Section: A Methodologymentioning

confidence: 99%

Section: Low-overhead Real-time Tracing and Benchmarking -4mentioning

confidence: 99%

Section: Low-overhead Real-time Tracing and Benchmarking -4mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

RobotCore: An Open Architecture for Hardware Acceleration in ROS 2

Mayoral-Vilches¹,

Neuman²,

Plancher³

et al. 2022

Preprint

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show abstract

“…Several efforts have created performance measuring tools for various facets of a ROS 2 system. The ros2_tracing framework was introduced based on Linux Trace Toolkit Next Generation (LTTng) that allows for detailed analysis of instrumented events in a running ROS 2 system [5]. Tracing analysis is extended by adding the ability to do message flow analysis by associating causally-linked tracing events in the ROS 2 computation graph [6].…”

Section: Related Workmentioning

confidence: 99%

Spatio-temporal voxel layer: A view on robot perception for the dynamic world

Macenski¹,

Tsai²,

Feinberg

2020

International Journal of Advanced Robotic Systems

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The spatio-temporal voxel grid is an actively maintained open-source project providing an improved three-dimensional environmental representation that has been garnering increased adoption in large, dynamic, and complex environments. We provide a voxel grid and the Costmap 2-D layer plug-in, Spatio-Temporal Voxel Layer, powered by a real-time sparse occupancy grid with constant time access to voxels which does not scale with the environment’s size. We replace ray-casting with a new clearing technique we dub frustum acceleration that does not assume a static environment and in practice, represents moving environments better. Our method operates at nearly 400% less CPU load on average while processing 9 QVGA resolution depth cameras as compared to the voxel layer. This technique also supports sensors such as three-dimensional laser scanners, radars, and additional modern sensors that were previously unsupported in the available ROS Navigation framework that has become staples in the roboticists’ toolbox. These sensors are becoming more widely used in robotics as sensor prices are driven down and mobile compute capabilities improve. The Spatio-Temporal Voxel Layer was developed in the open with community feedback over its development life cycle and continues to have additional features and capabilities added by the community. As of February 2019, the Spatio-Temporal Voxel Layer is being used on over 600 robots worldwide in warehouses, factories, hospitals, hotels, stores, and libraries. The open-source software can be viewed and installed on its GitHub page at https://github.com/SteveMacenski/spatio_temporal_voxel_layer .

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Atlas Fusion 2.0 A ROS2 Based Real-Time Sensor Fusion Framework

Svědiroh,

Žalud

2024

Lecture Notes in Computer Science

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ros2_tracing: Multipurpose Low-Overhead Framework for Real-Time Tracing of ROS 2

Cited by 27 publications

References 14 publications

RobotCore: An Open Architecture for Hardware Acceleration in ROS 2

RobotCore: An Open Architecture for Hardware Acceleration in ROS 2

Spatio-temporal voxel layer: A view on robot perception for the dynamic world

Atlas Fusion 2.0 A ROS2 Based Real-Time Sensor Fusion Framework

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