Efficient dynamic occupancy grid mapping using non-uniform cell representation

Buerkle, Cornelius; Oboril, Fabian; Jarquin, Julio; Scholl, Kay-Ulrich

doi:10.1109/iv47402.2020.9304571

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…Based on this principle, it is proposed to use a ray tracing technique to identify the possible force paths within each pair of aggregates connected by an element in the lattice network. This approach is somewhat similar to what has been used to compute static or dynamic occupancy grids in the fields of computer graphics (Han et al, 2019), acoustics (Sarradj, 2017), or autonomous driving in the context of real-time navigation and path planning (Buerkle et al, 2020;Yguel et al, 2008).…”

Section: Effective Resisting Areas Of the Lattice Elementsmentioning

confidence: 98%

Stochastic lattice discrete particle modeling of fracture in pervious concrete

Fascetti

Ichimaru

Bolander

2022

Computer aided Civil Eng

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This article presents a stochastic modeling approach for simulating the mechanical behavior of pervious concrete, based on novel extensions of the lattice discrete particle model. Selected digital images of the internal mesostructure, obtained from physical specimens, are used to survey material features and produce statistically representative descriptions of the pore networks. A procedure for estimating the statistical features of the mesostructure is proposed, and samples of a spatially correlated random field are utilized to numerically reproduce the distribution of the large, connected pores in the material. The numerical samples are linked to the topology of the lattice network by means of two novel techniques proposed herein: (1) a random placement procedure for the poly‐sized spheres representing coarse aggregate and (2) a ray tracing technique, which is used to evaluate the effective distributions of mass, stiffness, and strength of each lattice element according to the local distribution of porosity. Numerical results demonstrate how the proposed model is capable of simulating both the large scatter in strength and the variety of failure modes that are observed when testing physical specimens of pervious concrete. The proposed procedure is generally applicable to different types of materials with varying porosity, opening up new possibilities for the simulation of porous media by means of lattice discrete particle modeling techniques.

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Section: Effective Resisting Areas Of the Lattice Elementsmentioning

confidence: 98%

Stochastic lattice discrete particle modeling of fracture in pervious concrete

Fascetti

Ichimaru

Bolander

2022

Computer aided Civil Eng

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“…With parallelism and other optimization techniques the latency could therefore be reduced further if required. Also using a non-uniform grid representation [22] can improve the latency of the sensor checks.…”

Section: G Runtime Evaluationmentioning

confidence: 99%

Fault-Tolerant Perception for Automated Driving A Lightweight Monitoring Approach

Buerkle¹,

Geißler²,

Paulitsch³

et al. 2021

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While the most visible part of the safety verification process of automated vehicles concerns the planning and control system, it is often overlooked that safety of the latter crucially depends on the fault-tolerance of the preceding environment perception. Modern perception systems feature complex and often machine-learning-based components with various failure modes that can jeopardize the overall safety. At the same time, a verification by for example redundant execution is not always feasible due to resource constraints. In this paper, we address the need for feasible and efficient perception monitors and propose a lightweight approach that helps to protect the integrity of the perception system while keeping the additional compute overhead minimal. In contrast to existing solutions, the monitor is realized by a well-balanced combination of sensor checks -here using LiDAR information -and plausibility checks on the object motion history. It is designed to detect relevant errors in the distance and velocity of objects in the environment of the automated vehicle. In conjunction with an appropriate planning system, such a monitor can help to make safe automated driving feasible.

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“…However, with the development of higher-fidelity planning and control software, the need for more detailed maps has grown. This increase in desired map resolution combined with an increase in the resolution of sensors can cause even modern occupancy map solutions to become information bottlenecks as populating occupancy grids with dense point clouds is computationally expensive and is a problem still being explored today [3]. This is an issue further compounded in fields where agent size and computing power are restricted.…”

Section: Introductionmentioning

confidence: 99%

NanoMap: A GPU-Accelerated OpenVDB-Based Mapping and Simulation Package for Robotic Agents

2022

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Encoding sensor data into a map is a problem that must be undertaken by any robotic agent operating in unknown or uncertain environments, and real-time updates are crucial to safe planning and control. Most modern robotic sensors produce some form of depth data or point cloud information that is only useful to the agent after being processed into the appropriate data structure, oftentimes an occupancy map. However, as the quality of sensor technology improves, so does the magnitude of the input data, which can creates a problem when trying to construct occupancy maps in real-time. Populating such an occupancy map using these dense point clouds can quickly become an expensive process, and many robotic agents have limited onboard computational bandwidth and memory. This results in delayed map updates and reduced operational performance in dynamic environments where real-time information is crucial for safe operation. However, while many modern robotic agents are still relatively limited by the power of onboard central processing units (CPUs), many platforms are gaining access to onboard graphics processing units (GPUs), and these resources remain underutilised with respect to the problem of occupancy mapping. We propose a novel probabilistic mapping solution that leverages a combination of OpenVDB, NanoVDB, and Nvidia’s Compute Unified Device Architecture (CUDA) to encode dense point clouds into OpenVDB data structures, leveraging the parallel compute strength of GPUs to provide significant speed advantages and further free up resources for tasks that cannot as easily be performed in parallel. An evaluation of our solution is provided, with performance benchmarks provided for both a laptop and a low power single board computer with onboard GPU. Similar performance improvements should be accessible on any system with access to a CUDA-compatible GPU. Additionally, our library provides the means to simulate one or more sensors on an agent operating within a randomly generated 3D-grid environment and create a live map for the purposes of evaluating planning and control techniques and for training agents via deep reinforcement learning. We also provide interface packages for the Robotic Operating System (ROS1) and the Robotic Operating System 2 (ROS2), and a ROS2 visualisation (RVIZ2) plugin for the underlying OpenVDB data structure.

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Efficient dynamic occupancy grid mapping using non-uniform cell representation

Cited by 7 publications

References 9 publications

Stochastic lattice discrete particle modeling of fracture in pervious concrete

Stochastic lattice discrete particle modeling of fracture in pervious concrete

Fault-Tolerant Perception for Automated Driving A Lightweight Monitoring Approach

NanoMap: A GPU-Accelerated OpenVDB-Based Mapping and Simulation Package for Robotic Agents

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