A Robust Method for Detecting Parking Areas in Both Indoor and Outdoor Environments

Zong, Wenhao; Chen, Qijun

doi:10.3390/s18061903

Cited by 17 publications

(9 citation statements)

References 26 publications

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“…The variable l is the distance between the front and rear axis, v is the velocity of the rear axis center, δ is the steering angle, P is the instantaneous center of rotation, and r is the radius around P . See [37] for details.…”

Section: Sanity Checksmentioning

confidence: 99%

Simple But Effective Redundant Odometry for Autonomous Vehicles

Reinke,

Chen,

Stachniss

2021

Preprint

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Robust and reliable ego-motion is a key component of most autonomous mobile systems. Many odometry estimation methods have been developed using different sensors such as cameras or LiDARs. In this work, we present a resilient approach that exploits the redundancy of multiple odometry algorithms using a 3D LiDAR scanner and a monocular camera to provide reliable state estimation for autonomous vehicles. Our system utilizes a stack of odometry algorithms that run in parallel. It chooses from them the most promising pose estimation considering sanity checks using dynamic and kinematic constraints of the vehicle as well as a score computed between the current LiDAR scan and a locally built point cloud map. In this way, our method can exploit the advantages of different existing ego-motion estimating approaches. We evaluate our method on the KITTI Odometry dataset. The experimental results suggest that our approach is resilient to failure cases and achieves an overall better performance than individual odometry methods employed by our system. Odometry Proposals for each timestamp Odometry Output Sanity check + Score https://github.com/PRBonn/MutiverseOdometry

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Section: Sanity Checksmentioning

confidence: 99%

Simple But Effective Redundant Odometry for Autonomous Vehicles

Reinke,

Chen,

Stachniss

2021

Preprint

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“…In order to improve the accuracy and continuity of parking slot detection, a series of parking slot tracking methods 11–13 based on motion sensors were proposed. Suhr and Jung 11 fused the AVM videos and motion sensors-based odometry by directional chamfer matching to continuously predict the position of the parking slot.…”

Section: Related Workmentioning

confidence: 99%

“…Subsequently, in order to improve parking slot detection and tracking performance in the indoor parking lot, they improved the tracking algorithm by considering pillar information. 12 To solve the problem of light variations and shadows, Zong and Chen 13 proposed a parking space tracking algorithm based on the vehicle kinematics model and Kalman Filter. 21 However, these motion sensors-based tracking methods not only need extra motion senor but also produce cumulative errors.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, these methods are prone to miss some parking slots due to occlusion and limitations of vision during parking. To overcome the above limitations, a series of parking slot tracking methods 11–13 based on motion sensors were proposed. However, these motion sensors-based tracking methods not only need extra motion senor but also produce cumulative errors.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Vacant parking slot detection and tracking during driving and parking with a standalone around view monitor

Cao

Yan

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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Due to distortion, limitations of vision, and occlusion, most of the existing vacant parking slot detection methods with a standalone around view monitor (AVM) are prone to miss some parking slots and incorrectly identify whether the parking slot is vacant. To overcome this problem, we propose a complete method for vacant parking slot detection and tracking during driving and parking. Considering the different conditions of driving and parking, two different deep convolutional neural networks (DCNNs) are used to detect parking slots, of which the vacant parking slot detection network (VPS-Net) is used to detect vacant parking slots during driving, and the directional marking point detection network (DMPR-PS) is used to detect the directional marking points of the target parking slot during parking. Furthermore, in the driving process, we design a new matching rule and tracking management rule based on the Kernelized Correlation Filter (KCF) to track the parking slots, and fuse classification results of multiple frames to determine the occupancy status. In the parking process, since the parking slot is easily blocked by the vehicle, we design another new tracker to track the directional marking points and infer the complete parking slot using tracking results and prior geometric information. To evaluate the proposed method, a labeled video sequence dataset is established. Experiments show that the proposed method has improved the accuracy and continuity of vacant parking slots detection and positioning whether in the driving process or parking process.

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“…Using cameras as the primary sensors can help in addressing this issue, as cameras provide high-resolution information of the environment. [20] and [21] present techniques using a combination of camera and ultrasonic sensors, where cameras are used for slot marking detection and ultrasonic sensors are used for occupancy check. Figure 1 depicts sample images of parking slots under different conditions.…”

Section: Introductionmentioning

confidence: 99%

Camera Based Parking Slot Detection for Autonomous Parking

Suddamalla

Wong

Balaji

et al. 2021

Communications in Computer and Information Science

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Autonomous parking is one of the primary functionalities of intelligent vehicles. This requires the accurate detection of a free parking slot and its dimensions. Here we propose a method using a novel combination of computer vision techniques on the camera data mounted on sides of a car. Our occupancy check method is based on perspectivity guided motion segmentation, which makes it a generic approach for handling random obstacles without prior knowledge. When tested on our dataset collected in environments such as shadows, wet surface, indoors and outdoors with obstacles including cars, motorbikes, cones, carton boxes and trees, this method achieved a promising performance with F1 score higher than 97%. With the ability to run on low computational devices such as CPU, this method is adaptable to practical solutions for both AD and aftermarket ADAS systems.

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A Robust Method for Detecting Parking Areas in Both Indoor and Outdoor Environments

Cited by 17 publications

References 26 publications

Simple But Effective Redundant Odometry for Autonomous Vehicles

Simple But Effective Redundant Odometry for Autonomous Vehicles

Vacant parking slot detection and tracking during driving and parking with a standalone around view monitor

Camera Based Parking Slot Detection for Autonomous Parking

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