Towards autonomous driving in a parking garage: Vehicle localization and tracking using environment-embedded LIDAR sensors

Ibisch, André; Stümper, Stefan; Altinger, Harald; Neuhausen, Marcel; Tschentscher, Marc; Schlipsing, Marc; Salinen, Jan; Knoll, Alois

doi:10.1109/ivs.2013.6629569

Cited by 63 publications

(29 citation statements)

References 5 publications

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“…The idea is to reduce the uncertainty of the SLAM with a known static map thus improving the accuracy of localization. Instead of mounting LiDAR sensor on vehicles, the approach in [7] proposes to embed LiDAR sensors in the environment. Using RANSAC algorithm, the authors have successfully localized vehicles within 8cm of mean errors.…”

Section: State Of the Artmentioning

confidence: 99%

Wifi fingerprinting localization for intelligent vehicles in car park

Nguyen

Charette

Nashashibi

et al. 2018

2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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In this paper, a novel method of WiFi fingerprinting for localizing intelligent vehicles in GPS-denied area, such as car parks, is proposed. Although the method itself is a popular approach for indoor localization application, adapting it to the speed of vehicles requires different treatment. By deploying an ensemble neural network for fingerprinting classification, the method shows a reasonable localization precision at car park speed. Furthermore, a Gaussian Mixture Model (GMM) Particle Filter is applied to increase localization frequency as well as accuracy. Experiments show promising results with average localization error of 0.6m.

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Section: State Of the Artmentioning

confidence: 99%

Wifi fingerprinting localization for intelligent vehicles in car park

Nguyen

Charette

Nashashibi

et al. 2018

2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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“…f and (o u , o v ) are the focal length in pixels and the principal point, respectively. If t goes to infinity in (9) and (10), u(t) and v(t) will converge to the principal point (o u , o v ). This means that the 3-D lines parallel to the optical axis of the camera pass through the principal point.…”

Section: B Pillar-based Free Space Detectionmentioning

confidence: 99%

“…However, these methods cannot easily be commercialized as they utilize accurate range-finding sensors (laser scanners [8], [9] and light strip projection [10]) that have not been adopted by mass produced vehicles or infrastructures due to problems related to cost, design, and durability. Therefore, this paper proposes a method that reliably detects and tracks vacant parking spaces in underground and indoor environments by fusing only those sensors already installed on mass produced vehicles: an AVM system, ultrasonic sensors, and in-vehicle motion sensors.…”

Section: Introductionmentioning

confidence: 99%

Automatic Parking Space Detection and Tracking for Underground and Indoor Environments

Suhr

Jung

2016

IEEE Trans. Ind. Electron.

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Even though many public parking lots are located underground and indoors, most existing automatic parking space detection and tracking methods have difficulty handling such scenarios due to severe illumination and complex obstacle conditions. To overcome this problem, this paper proposes a method that detects and tracks parking spaces in underground and indoor environments by fusing sensors already mounted on mass produced vehicles. The proposed detection method finds parking spaces based on a high-level fusion of two complementary approaches: parking slot marking-based and free space-based. Parking slots are detected by estimating parallel line pairs and free spaces are detected by recognizing the positions of parked vehicles as well as pillars. The proposed tracking method enhances the previous method by considering pillar information. Since pillars degrade parking slot tracking performance, this method estimates a pillar region and utilizes it to remove false edges and to estimate the amount of occlusion.Index Terms-Automatic parking system, Parking space detection and tracking, Underground and indoor, Sensor fusion. Jae Kyu Suhr (M'12) received his BS degree in electronic engineering at

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“…They focused on filtering out road curbs and other driving cars on street as noise [7]. Ibisch et al employed RANSAC and Kalman Filters in tracking parking through multiple Lidar sensors embedded in a parking garage in the lack of GPS information [8].…”

Section: Related Workmentioning

confidence: 99%

A Statistical Method for Parking Spaces Occupancy Detection via Automotive Radars

Luo

Saigal

Hampshire

et al. 2017

2017 IEEE 85th Vehicular Technology Conference (VTC Spring)

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Abstract-Real-time parking occupancy information is valuable for guiding drivers' searching for parking spaces. Recently many parking detection systems using range-based on-vehicle sensors are invented, but they disregard the practical difficulty of obtaining access to raw sensory data which are required for any feature-based algorithm. In this paper, we focus on a system using short-range radars (SRR) embedded in Advanced Driver Assistance System (ADAS) to collect occupancy information, and broadcast it through a connected vehicle network. The challenge that the data transmitted through ADAS unit has been encoded to sparse points is overcome by a statistical method instead of feature extractions. We propose a two-step classification algorithm combining Mean-Shift clustering and Support Vector Machine to analyze SRR-GPS data, and evaluate it through field experiments. The results show that the average Type I error rate for off-street parking is 15.23% and for on-street parking is 32.62%. In both cased the Type II error rates are less than 20%. Bayesian updating can recursively improve the mapping results. This paper can provide a comprehensive method to elevate automotive sensors for the parking detection function.

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Towards autonomous driving in a parking garage: Vehicle localization and tracking using environment-embedded LIDAR sensors

Cited by 63 publications

References 5 publications

Wifi fingerprinting localization for intelligent vehicles in car park

Wifi fingerprinting localization for intelligent vehicles in car park

Automatic Parking Space Detection and Tracking for Underground and Indoor Environments

A Statistical Method for Parking Spaces Occupancy Detection via Automotive Radars

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