Combined Lane Mapping Using a Mobile Mapping System

Wan, Rui; Huang, Yuchun; Xie, Rongchang; Ping, Ma

doi:10.3390/rs11030305

Cited by 17 publications

(9 citation statements)

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“…Previously, proposed approaches tackled the particular properties of traffic signs (i.e., retro-reflectivity for night-time visibility, colour, shape, size, height, orientation, planarity, and verticality), usually following safety standards. These properties require traffic signs to be treated as different objects from traffic lights [11][12][13], poles [14], lanes [15][16][17], trees [18], and other objects present in roads. A review of approaches depending on the object can be found in [19].…”

Section: Related Workmentioning

confidence: 99%

Novel Approach to Automatic Traffic Sign Inventory Based on Mobile Mapping System Data and Deep Learning

et al. 2020

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Traffic signs are a key element in driver safety. Governments invest a great amount of resources in maintaining the traffic signs in good condition, for which a correct inventory is necessary. This work presents a novel method for mapping traffic signs based on data acquired with MMS (Mobile Mapping System): images and point clouds. On the one hand, images are faster to process and artificial intelligence techniques, specifically Convolutional Neural Networks, are more optimized than in point clouds. On the other hand, point clouds allow a more exact positioning than the exclusive use of images. The false positive rate per image is only 0.004. First, traffic signs are detected in the images obtained by the 360° camera of the MMS through RetinaNet and they are classified by their corresponding InceptionV3 network. The signs are then positioned in the georeferenced point cloud by means of a projection according to the pinhole model from the images. Finally, duplicate geolocalized signs detected in multiple images are filtered. The method has been tested in two real case studies with 214 images, where 89.7% of the signals have been correctly detected, of which 92.5% have been correctly classified and 97.5% have been located with an error of less than 0.5 m. This sequence, which combines images to detection–classification, and point clouds to geo-referencing, in this order, optimizes processing time and allows this method to be included in a company’s production process. The method is conducted automatically and takes advantage of the strengths of each data type.

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Section: Related Workmentioning

confidence: 99%

Novel Approach to Automatic Traffic Sign Inventory Based on Mobile Mapping System Data and Deep Learning

et al. 2020

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“…Automated methods for mobile mapping to generate inventories of urban objects at large scale have received significant attention lately [1][2][3][4][5]. While most systems have laser scanners as a major part of their measurement device, a significant number of research efforts try to match objects across multiple views based solely on imagery.…”

Section: Introductionmentioning

confidence: 99%

Multi-View Instance Matching with Learned Geometric Soft-Constraints

Nassar

Lefèvre

Wegner

2020

IJGI

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We present a new approach for matching urban object instances across multiple ground-level images for the ultimate goal of city-scale mapping of objects with high positioning accuracy. What makes this task challenging is the strong change in view-point, different lighting conditions, high similarity of neighboring objects, and variability in scale. We propose to turn object instance matching into a learning task, where image-appearance and geometric relationships between views fruitfully interact. Our approach constructs a Siamese convolutional neural network that learns to match two views of the same object given many candidate image cut-outs. In addition to image features, we propose utilizing location information about the camera and the object to support image evidence via soft geometric constraints. Our method is compared to existing patch matching methods to prove its edge over state-of-the-art. This takes us one step closer to the ultimate goal of city-wide object mapping from street-level imagery to benefit city administration.

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“…A mobile mapping system (MMS) integrates measurement sensors such as a position and orientation system (POS), a laser scanning system and a charge-coupled device (CCD) camera on a mobile carrier [1,2], enabling the fast and efficient acquisition of spatial data on surrounding targets, which is widely used in three-dimensional modeling, digital city, street view map, digital mine and other fields [3][4][5][6]. In the measurement process, MMS needs the high-precision position and orientation information obtained by POS to realize the conversion from a laser scanner coordinates system to the specified mapping coordinate system.…”

Section: Introductionmentioning

confidence: 99%

A Positioning Error Compensation Method for a Mobile Measurement System Based on Plane Control

Shi

Zhang

Yang

et al. 2020

Sensors

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Global navigation satellite system (GNSS)/inertial navigation system (INS) navigation technology is one of the core technologies in a mobile measurement system and can provide real-time geo-referenced information. However, in the environment measurements, buildings cover up the GNSS signal, causing satellite signals to experience loss-of-lock. At this time errors of INS independent navigation accumulate rapidly, so it cannot meet the needs of the mobile measurement system. In this paper, a positioning error compensation method based on plane control is proposed by analyzing the error characteristics of position and orientation caused by satellite signal loss-of-lock in the urban environment. This method control uses planar features and the laser point cloud positioning equation to establish an adjustment model that ignores the influence of the attitude error and finds the positioning error at the middle point of the GNSS signal loss-of-lock time period, and then compensates for the error at other points by using the characteristics of the positioning error. The experimental results show that the accuracy of the compensated laser point cloud has been significantly improved, and the feasibility of the method is verified. Meanwhile, the method can rely on the existing building plane so the method is adaptable and easy to implement.

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Combined Lane Mapping Using a Mobile Mapping System

Cited by 17 publications

References 50 publications

Novel Approach to Automatic Traffic Sign Inventory Based on Mobile Mapping System Data and Deep Learning

Novel Approach to Automatic Traffic Sign Inventory Based on Mobile Mapping System Data and Deep Learning

Multi-View Instance Matching with Learned Geometric Soft-Constraints

A Positioning Error Compensation Method for a Mobile Measurement System Based on Plane Control

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