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This paper is concerned with the Simultaneous Localization And Mapping (SLAM) application with a mobile robot moving in a structured environment using data obtained from rotating sensors such as radars or lasers. A line-based EKF-SLAM (EKF stands for Extended Kalman Filter) algorithm is presented, which is able to deal with data that cannot be considered instantaneous when compared with the dynamics...
In urban environment, pedestrian detection is a challenging task in automotive research, which often suffers from the lack of reliability due to the occurrences of spurious detections. In order to answer multitarget multisensor tracking problem and more specifically pedestrian tracking, we propose to use an algorithm based on a stochastic recursive Bayesian framework also called particle filter. We...
Pedestrians are the most vulnerable participants in urban traffic. The first step toward protecting pedestrians is to reliably detect them in a real-time framework. In this paper, a new approach is presented for pedestrian detection in urban traffic conditions using a multilayer laser sensor mounted onboard a vehicle. This sensor, which is placed on the front of a vehicle, collects information about...
This paper is concerned with the simultaneous localization and mapping (SLAM) application using data obtained from a microwave radar sensor. The radar scanner is based on the frequency modulated continuous wave (FMCW) technology. In order to overcome the complexity of radar image analysis, a trajectory-oriented EKF-SLAM technique using data from a 360?? field of view radar sensor has been developed...
This article deals with the detection and tracking of pedestrians by means of a laser sensor. This sensor placed on the front of a vehicle collects information about distance distributed according to 4 laser planes. Like a vehicle, a pedestrian constitutes in urban environment an obstacle which must be detected, located, then identified and tracked if necessary. In order to improve the robustness...
In urban environments, pedestrian detection is a challenging task for automotive research, where algorithms suffer from a lack of reliability due to many false detections. This paper presents a multisensor fusion method based on a stochastic recursive Bayesian framework also called particle filter which fuses information from laser and video sensors to improve the performance of a pedestrian detection...
This paper deals with the simultaneous localization and map building (SLAM) problem using an implementation of the polynomial extended Kalman filter (PEKF). The proposed PEKF implementation is a filtering algorithm which is a polynomial transformation of state evolution and measurement equations. The performances of the algorithm have been evaluated through simulations. The comparison with the standard...
This paper introduces an implementation of the polynomial extended Kalman filter (PEKF) to solve the simultaneous localization and Map building (SLAM) problem. The proposed solution is a filtering algorithm which is a polynomial transformation of state evolution and measurement equations. The performances of the algorithm have been evaluated through two simulation runs. The first ones underline consistency...
Pedestrian safety is a primary traffic issue in urban environment. This article deals with the detection of pedestrians by means of a laser sensor. This sensor, placed on the front of a vehicle collects information about distance distributed according to 4 laser planes. Like a vehicle, a pedestrian constitutes in the vehicle environment an obstacle which must be detected, located, then identified...
This paper presents a method for fusing two maps of an environment: one estimated with an application of the simultaneous localization and mapping (SLAM) concept and the other one known a priori by a vehicle. The goal of such an application is double: first, to estimate the vehicle pose in this known map and, second, to constrain the map estimate with the known map using an implementation of the local...
This article deals with the following issue: how to track a varying number of pedestrians through observations by means of a 4-plane laser sensor. In order to answer to the multiple target tracking problem and more specifically pedestrian tracking, we propose in this paper a statistical approach using a particle filter based on nonparametric data association methods. This approach allows to go beyond...
This article deals with the detection of pedestrians by means of a laser sensor. This sensor placed on the front of a vehicle collects information about distance distributed according to 4 horizontal planes. Like a vehicle, a pedestrian constitutes in the vehicle environment an obstacle which must be detected, located, then identified and tracked if necessary. In order to improve the robustness of...
This paper deals with the assessment of centralized fusion for two dissimilar sensors for the purpose of tracking road obstacles. The aim of sensor fusion is to produce an improved estimated state of a system from a set of independent data sources. Indeed, for a robust perception of the environment, seen here as obstacles, several sensors should be installed in the equipped vehicle: camera, lidar,...
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