Large-scale mapping in complex field scenarios using an autonomous car

Mutz, Filipe; Veronese, Lucas P.; Oliveira-Santos, Thiago; Aguiar, Edilson de; Cheein, Fernando Auat; Souza, Alberto F. De

doi:10.1016/j.eswa.2015.10.045

Cited by 81 publications

(52 citation statements)

References 31 publications

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“…It is only after this data is collected, followed by heavy postprocessing to create the final HD map, that a vehicle can drive autonomously using the precise map in the given environment. While recent developments utilizing a pose‐based GraphSLAM estimator provide even more precise mapping, these approaches remain highly time, cost, and labor intensive . An alternative method of obtaining an HD map of the environment involves the use of unmanned aerial vehicles, equipped with lidar and camera systems, as a novel platform for photogrammetry .…”

Section: Introductionmentioning

confidence: 99%

“…While recent developments utilizing a pose-based GraphSLAM estimator provide even more precise mapping, these approaches remain highly time, cost, and labor intensive. 9 An alternative method of obtaining an HD map of the environment involves the use of unmanned aerial vehicles, equipped with lidar and camera systems, as a novel platform for photogrammetry. 10 However, this method also requires time-consuming data collection with heavy data postprocessing.…”

Section: Introductionmentioning

confidence: 99%

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Estimation and navigation methods with limited information for autonomous urban driving

Chipka

Campbell

2019

Engineering Reports

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Autonomous driving in dense urban areas presents an especially difficult task. First, globally localizing information, such as GPS signal, often proves to be unreliable in such areas due to signal shadowing and multipath errors. Second, the high‐definition environmental maps with sufficient information for autonomous navigation require a large amount of data to be collected from these areas, significant postprocessing of this data to generate the map, and then continual maintenance of the map to account for changes in the environment. This paper addresses the issue of autonomous driving in urban environments by investigating algorithms and an architecture to enable fully functional autonomous driving with little to no reliance on map‐based measurements or GPS signals. An extended Kalman filter with odometry, compass, and sparse landmark measurements as inputs is used to provide localization. Real‐time detection and estimation of key roadway features are used to create an understanding of the surrounding static scene. Navigation is accomplished by a compass‐based navigation control law. Experimental scene understanding results are obtained using computer vision and estimation techniques and demonstrate the ability to probabilistically infer key features of an intersection in real time. Key results from Monte Carlo studies demonstrate the proposed localization and navigation methods. These tests provide success rates of urban navigation under different environmental conditions, such as landmark density, and show that the vehicle can navigate to a goal nearly 10 km away without any external pose update at all. Field tests validate these simulated results and demonstrate that, for given test conditions, an expected range can be determined for a given success rate.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation and navigation methods with limited information for autonomous urban driving

Chipka

Campbell

2019

Engineering Reports

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“…IARA's hardware is composed of a Ford Scape Hybrid retrofitted with an assortment of sensors and processing units. Its software is composed of many modules, which includes a mapper [16], a localizer [17], a path planner, a behavior selector, a motion planner [18], an obstacle avoider [19], and a controller [20]. Fig.…”

Section: Introductionmentioning

confidence: 99%

Bio-Inspired Foveated Technique for Augmented-Range Vehicle Detection Using Deep Neural Networks

Azevedo

Panceri

Guidolini

et al. 2019

2019 International Joint Conference on Neural Networks (IJCNN)

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We propose a bio-inspired foveated technique to detect cars in a long range camera view using a deep convolutional neural network (DCNN) for the IARA self-driving car. The DCNN receives as input (i) an image, which is captured by a camera installed on IARA's roof; and (ii) crops of the image, which are centered in the waypoints computed by IARA's path planner and whose sizes increase with the distance from IARA. We employ an overlap filter to discard detections of the same car in different crops of the same image based on the percentage of overlap of detections' bounding boxes. We evaluated the performance of the proposed augmented-range vehicle detection system (ARVDS) using the hardware and software infrastructure available in the IARA self-driving car. Using IARA, we captured thousands of images of real traffic situations containing cars in a long range. Experimental results show that ARVDS increases the Average Precision (AP) of long range car detection from 29.51% (using a single whole image) to 63.15%.

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“…The IARA's software is composed of six main modules: behavior selector, localizer [13] [14], mapper [15], path planner, motion planner (the focus of this paper) and obstacle avoider [12]. Together they allow IARA's autonomous operation in urban roads.…”

Section: A Iara's Hardware and Softwarementioning

confidence: 99%

A Model-Predictive Motion Planner for the IARA autonomous car

Cardoso

Oliveira

Teixeira

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-We present the Model-Predictive Motion Planner (MPMP) of the Intelligent Autonomous Robotic Automobile (IARA). IARA is a fully autonomous car that uses a path planner to compute a path from its current position to the desired destination. Using this path, the current position, a goal in the path and a map, IARA's MPMP is able to compute smooth trajectories from its current position to the goal in less than 50 ms. MPMP computes the poses of these trajectories so that they follow the path closely and, at the same time, are at a safe distance of eventual obstacles. Our experiments have shown that MPMP is able to compute trajectories that precisely follow a path produced by a Human driver (distance of 0.15 m in average) while smoothly driving IARA at speeds of up to 32.4 km/h (9 m/s).

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Large-scale mapping in complex field scenarios using an autonomous car

Cited by 81 publications

References 31 publications

Estimation and navigation methods with limited information for autonomous urban driving

Estimation and navigation methods with limited information for autonomous urban driving

Bio-Inspired Foveated Technique for Augmented-Range Vehicle Detection Using Deep Neural Networks

A Model-Predictive Motion Planner for the IARA autonomous car

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