A Random Finite Set Approach for Dynamic Occupancy Grid Maps with Real-Time Application

Nuß, Dominik; Reuter, Stephan; Thom, Markus; Yuan, Ting; Krehl, Gunther; Maile, Michael D.; Gern, Axel; Dietmayer, Klaus

doi:10.48550/arxiv.1605.02406

Cited by 6 publications

(13 citation statements)

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“…A dynamic occupancy grid map (DOGMa) [16], fed by multiple sensors, can provide a bird's eye image like 360 • representation of the environment, as illustrated in Fig. 1.…”

Section: Filtered Dynamic Inputmentioning

confidence: 99%

“…In general, the algorithm exploits sensor dependent strengths using engineered models. These models can, e.g., consider free space gained from a laser measurement, or ego motion compensated Doppler velocities from radars [16]. If sensor specifications change, parameters can easily be adapted.…”

Section: Filtered Dynamic Inputmentioning

confidence: 99%

“…The filter algorithm runs in two modules: In the first, sensor data is converted into classical occupancy grid maps [17], [18] using sensor specific measurement models. These grids serve as a generic data interface to the second module, a particle filter estimating the spatial occupancy and velocity distribution [16]. Each grid cell is updated independently and no interaction between cells is modeled.…”

Section: Filtered Dynamic Inputmentioning

confidence: 99%

“…1. The DOGMa uses Bayesian filtering [16] to fuse a variety of sensors and represents the entire environment, static and dynamic, in a bird's eye view. Each multichannel pixel, or cell, contains probabilistic occupancy and velocity estimates.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling

Hoermann

Bach

Dietmayer

2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

146

109

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Long-term situation prediction plays a crucial role in the development of intelligent vehicles. A major challenge still to overcome is the prediction of complex downtown scenarios with multiple road users, e.g., pedestrians, bikes, and motor vehicles, interacting with each other. This contribution tackles this challenge by combining a Bayesian filtering technique for environment representation, and machine learning as long-term predictor. More specifically, a dynamic occupancy grid map is utilized as input to a deep convolutional neural network. This yields the advantage of using spatially distributed velocity estimates from a single time step for prediction, rather than a raw data sequence, alleviating common problems dealing with input time series of multiple sensors. Furthermore, convolutional neural networks have the inherent characteristic of using context information, enabling the implicit modeling of road user interaction. Pixel-wise balancing is applied in the loss function counteracting the extreme imbalance between static and dynamic cells. One of the major advantages is the unsupervised learning character due to fully automatic label generation. The presented algorithm is trained and evaluated on multiple hours of recorded sensor data and compared to Monte-Carlo simulation.

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“…A dynamic occupancy grid map (DOGMa) [16], fed by multiple sensors, can provide a bird's eye image like 360 • representation of the environment, as illustrated in Fig. 1.…”

Section: Filtered Dynamic Inputmentioning

confidence: 99%

Section: Filtered Dynamic Inputmentioning

confidence: 99%

Section: Filtered Dynamic Inputmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling

Hoermann

Bach

Dietmayer

2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

146

109

View full text Add to dashboard Cite

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“…This filter uses three types of occupancies including occupied, free, and unknown: m k ∈ {O(1), F(0),U(0.5)} to characterize a map m. From the initial distribution of the occupancy grid which is set to 0.5 for all cells, sequential sensor measurements can update the occupancy grid using inverse sensor models p z (m|z) for every time step. For the grid within the field of view (FOV), the posterior occupancy probability at time k + 1, p k+1 (m k+1 ) can be obtained via the equation [20]:…”

Section: B Map Representationmentioning

confidence: 99%

An Architecture for Person-Following using Active Target Search

Kim¹,

Arduengo²,

Walker³

et al. 2018

Preprint

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This paper addresses a novel architecture for person-following robots using active search. The proposed system can be applied in real-time to general mobile robots for learning features of a human, detecting and tracking, and finally navigating towards that person. To succeed at personfollowing, perception, planning, and robot behavior need to be integrated properly. Toward this end, an active target searching capability, including prediction and navigation toward vantage locations for finding human targets, is proposed. The proposed capability aims at improving the robustness and efficiency for tracking and following people under dynamic conditions such as crowded environments. A multi-modal sensor information approach including fusing an RGB-D sensor and a laser scanner, is pursued to robustly track and identify human targets. Bayesian filtering for keeping track of human and a regression algorithm to predict the trajectory of people are investigated. In order to make the robot autonomous, the proposed framework relies on a behavior-tree structure. Using Toyota Human Support Robot (HSR), real-time experiments demonstrate that the proposed architecture can generate fast, efficient person-following behaviors.

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Dynamic Hilbert Maps: Real-Time Occupancy Predictions in Changing Environments

Guizilini

Senanayake

Ramos

2019

2019 International Conference on Robotics and Automation (ICRA)

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This paper addresses the problem of learning instantaneous occupancy levels of dynamic environments and predicting future occupancy levels. Due to the complexity of most real-world environments, such as urban streets or crowded areas, the efficient and robust incorporation of temporal dependencies into otherwise static occupancy models remains a challenge. We propose a method to capture the spatial uncertainty of moving objects and incorporate this uncertainty information into a continuous occupancy map represented in a rich high-dimensional feature space. Experiments performed using LIDAR data verified the real-time performance of the algorithm.

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A Random Finite Set Approach for Dynamic Occupancy Grid Maps with Real-Time Application

Cited by 6 publications

References 0 publications

Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling

Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling

An Architecture for Person-Following using Active Target Search

Dynamic Hilbert Maps: Real-Time Occupancy Predictions in Changing Environments

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