Maan El Badaoui El Najjar

A Road-Matching Method for Precise Vehicle Localization Using Belief Theory and Kalman Filtering

This paper describes a novel road-matching method designed to support the real-time navigational function of cars for advanced systems applications in the area of driving assistance. This method provides an accurate estimation of position for a vehicle relative to a digital road map using Belief Theory and Kalman filtering. Firstly, an Extended Kalman Filter combines the DGPS and ABS sensor measurements to produce an approximation of the vehicle’s pose, which is then used to select the most likely segment from the database. The selection strategy merges several criteria based on distance, direction and velocity measurements using Belief Theory. A new observation is then built using the selected segment, and the approximate pose adjusted in a second Kalman filter estimation stage. The particular attention given to the modeling of the system showed that incrementing the state by the bias (also called absolute error) of the map significantly increases the performance of the method. Real experimental results show that this approach, if correctly initialized, is able to work over a substantial period without GPS.

Road Selection Using Multicriteria Fusion for the Road-Matching Problem

This paper presents a road selection strategy for novel road-matching methods that are designed to support real-time navigational features within Advanced Driving-Assistance Systems (ADAS). Selecting the most likely segment(s) is a crucial issue for the road-matching problem. The selection strategy merges several criteria using Belief theory. Particular attention is given to the development of belief functions from measurements and estimations of relative distances, headings, and velocities. Experimental results using data from antilock brake system sensors, the differential Global Positioning System receiver, and the accurate digital roadmap illustrate the performances of this approach, particularly in ambiguous situations

Virtual 3D City Model for Navigation in Urban Areas

In this paper, we propose to study the integration of a new source of a priori information, which is the virtual 3D city model. We study this integration for two tasks: vehicles geo-localization and obstacles detection. A virtual 3D city model is a realistic representation of the evolution environment of a vehicle. It is a database of geographical and textured 3D data. We describe an ego-localization method that combines measurements of a GPS (Global Positioning System) receiver, odometers, a gyrometer, a video camera and a virtual 3D city model. GPS is often consider as the main sensor for localization of vehicles. But, in urban areas, GPS is not precise or even can be unavailable. So, GPS data are fused with odometers and gyrometer measurements using an Unscented Kalman Filter (UKF). However, during long GPS unavailability, localization with only odometers and gyrometer drift. Thus, we propose a new observation of the location of the vehicle. This observation is based on the matching between the current image acquired by an on-board camera and the virtual 3D city model of the environment. We also propose an obstacle detection method based on the comparison between the image acquired by the on-board camera and the image extracted from the 3D model. The following principle is used: the image acquired by the on-board camera contains the possible dynamic obstacles whereas they are absent from the 3D model. The two proposed concepts are tested on real data.

Intelligent Geolocalization in Urban Areas Using Global Positioning Systems, Three-Dimensional Geographic Information Systems, and Vision

This article tackles the problem of a vehicles geolocalization in urban areas. For this purpose, Global Positioning System (GPS) receiver is the main sensor. However, the use of GPS alone is not sufficient in many urban environments. GPS has to be helped with dead-reckoned sensors, map data, and cameras. A novel observation of the absolute pose of the vehicle is proposed to back up GPS and the drift of dead-reckoned sensors. This approach uses a new source of information that is a geographical 3-dimensional (3D) model of the environment in which the vehicle navigates. This virtual 3D city model is managed in real time by a 3D geographical information system (3D GIS). This poses observation is constructed by matching the virtual image provided by the 3D GIS and the real image acquired by an onboard camera. An extended Kalman filter combines the sensors measurements to produce an estimation of the vehicles pose. Experimental results using data from an odometer, a gyroscope, a GPS receiver, a camera, and an accurate geographical 3D model of the environment illustrate the developed approach.

Harris, SIFT and SURF features comparison for vehicle localization based on virtual 3D model and camera

This paper proposed a new vehicle geo-localization method in urban environment integrating a new source of information that is a virtual 3D city model. This 3D model provides a realistic representation of the navigation environment of the vehicle. To optimize the performance of vehicle geo-localization system, several sources of information are integrated for their complementarity and redundancy: a GPS receiver, proprioceptive sensors (odometers and gyrometer), a video camera and a virtual 3D city model. The pose estimation algorithm used to fuse the different sensors data is an IMM-UKF (Interacting Multiple Model - Unscented Kalman Filter). The proprioceptive sensors allow to continuously estimating the dead-reckoning position and orientation of the vehicle. This dead-reckoning estimation of the pose is corrected by GPS measurements. Moreover, a 3D model/camera based observation of the vehicle pose is constructed to compensate the drift of the dead-reckoning localization when GPS measurements are unavailable for a long time. This pose observation is based on the matching between the virtual image extracted from the 3D city model and the real image acquired by the camera. The observation construction is composed of two major parts. The first part consists in detecting and matching the feature points of the real and virtual images. Three features are compared: Harris corner, SIFT (Scale Invariant Feature Transform) and SURF (Speed Up Robust Features). The second part is the pose computation using POSIT algorithm and the previously matched features set. The developed approach has been tested on a real sequence and the obtained results proved the feasibility and robustness of the approach.

Vehicle geo-localization based on IMM-UKF data fusion using a GPS receiver, a video camera and a 3D city model

Vehicle geo-localization in urban areas remains to be challenging problems. For this purpose, Global Positioning System (GPS) receiver is usually the main sensor. But, the use of GPS alone is not sufficient in many urban environments due to wave multi-path. In order to provide accurate and robust localization, GPS has so to be helped with other sensors like dead-reckoned sensors, map data, cameras or LIDAR. In this paper, a new observation of the absolute pose of the vehicle is proposed to back up GPS measurements. The proposed approach exploits a virtual 3D model managed by a 3D geographical information system (3D GIS) and a video camera. The concept is to register the acquired image to the 3D model that is geo-localized. For that, two images have to be matched: the real image and the virtual image. The real image is acquired by the on board camera and provides the real view of the scene viewed by the vehicle. The virtual image is provided by the 3D GIS. The developed method is composed of three parts. The first part consists in detecting and matching the feature points of the real image and of the virtual image. Two methods: SIFT (Scale Invariant Feature Transform) and Harris corner detector are compared. The second part concerns the position computation using POSIT algorithm and the previously matched features set. The third part concerns the data fusion using IMM-UKF (Interacting Multiple Model-Unscented Kalman Filter). The proposed approach has been tested on a real sequence and the obtained results proved the feasibility and robustness of the approach.

Tracking System using GPS, vision and 3D virtual Model

In order to improve the vehicle tracking quality in the cities and especially in urban area, the following article handles the correspondence between real and virtual images to find the closest virtual image to the real one. Real image are extracted from cameras equipped by a GPS system, together installed in the vehicle. Virtual images are extracted using a GPS from a database managed by 3D geographical information system (3D- GIS). It is known that GPS cannot give accurately the coordinates of a vehicle so it is necessary to use other kind of information using embedded sensors like camera. A way to compute a position using vision is to find the closest image in a 3D cartographical database which corresponds to the real one seen by the camera. Two methods are developed and tested with real data : the first method uses the Hough transform where each line corresponds to a point in the polar coordinate then we compare the image transformations. The second method is based on the Ransac fitting homography method. This method based on taking the two images real and virtual image, find the corners of each image using a harris corner detector, use the maximally correlated points to connect them, robustly fits a homography to a set of putatively matched image points, find the number of putatively matched image points that are called inliers and the greatest the number of inliers the closer is the virtual image to a real one. It uses homography, harris corner detector, and correlation functions. Results with real data are presented to illustrate performance of developed method.

A Hybrid Bayesian Framework for Map Matching: Formulation Using Switching Kalman Filter

This paper addresses an important issue for intelligent transportation system, namely the ability of vehicles to safely and reliably localize themselves within an a priori known road map network. For this purpose, we propose an approach based on hybrid dynamic bayesian networks enabling to implement in a unified framework two of the most successful families of probabilistic model commonly used for localization: linear Kalman filters and Hidden Markov Models. The combination of these two models enables to manage and manipulate multi-hypotheses and multi-modality of observations characterizing Map Matching problems and it improves integrity approach. Another contribution of the paper is a chained-form state space representation of vehicle evolution which permits to deal with non-linearity of the used odometry model. Experimental results, using data from encoders’ sensors, a DGPS receiver and an accurate digital roadmap, illustrate the performance of this approach, especially in ambiguous situations.

Fault tolerant collaborative localization for multi-robot system

Multi-robot system is used in some unreachable or dangerous area in order to replace the human operators. In such environments the integrity of localization should be assured by adding a sensor fault diagnosis step. In this paper, we present a method able, in addition of localizing a group of robots, to detect and exclude the faulty sensors from the team. The estimator is the informational form of the Kalman Filter (KF) namely Information Filter (IF). The developed residual test is based on the divergence between the predicted and the corrected estimation of the IF, calculated in term of the Kullback-Leibler divergence (KLD). The main contributions of this paper: - developing a method able simultaneously to localize a group of robots and to detect the faulty sensors - using the IF and the KLD as a residual test - Application of the proposed framework to a real environment with real robots.

Fault detection and exclusion method for a tightly coupled localization system

Integrity monitoring for a positioning method permit us to guarantee a high integrity localization which is needed for an autonomous navigation system. Different approaches for localization integrity monitoring have been developed. In this paper, we propose a Fault Detection and Exclusion (FDE) method based on information metrics since it provides tools that allow designing residual test that increase the integrity of localization. A residual test based on the Kullback-Leibler divergence (KLD) is elaborated. It is integrated in a FDE architecture applied to localization using a tightly coupled multi-sensor (GPS and odometer) data fusion method.