Probabilistic Integration of Intensity and Depth Information for Part-Based Vehicle Detection

International audienceIn this paper, an object class recognition method is presented. The method uses local image features and follows the part-based detection approach. It fuses intensity and depth information in a probabilistic framework. The depth of each local feature is used to weigh the probability of finding the object at a given distance. To train the system for an object class, only a database of images annotated with bounding boxes is required, thus automatizing the extension of the system to different object classes. We apply our method to the problem of detecting vehicles from a moving platform. The experiments with a data set of stereo images in an urban environment show a significant improvement in performance when using both information modalities

Advances in the Bayesian Occupancy Filter framework using robust motion detection technique for dynamic environment monitoring

International audienceThe Bayesian Occupancy Filter provides a framework for grid-based monitoring of the dynamic environment. It allows to estimate dynamic grids, containing both information of occupancy and velocity. Clustering such grids then provides detection of the objects in the observed scene. In this paper we present recent improvements in this framework. First, multiple layers from a laser scanner are fused using opinion pool, to deal with conflicting information. Then a fast motion detection technique based on laser data and odometer/IMU information is used to separate the dynamic environment from the static one. This technique instead of performing a complete SLAM (Simultaneous Localization and Mapping) solution, is based on transferring occupancy information between consecutive data grids, the objective is to avoid false positives (static objects) like other DATMO approaches. Finally, we show the integration with Bayesian Occupancy Filter (BOF) and with the subsequent tracking module called Fast Clustering-Tracking Algorithm (FCTA). We especially show the improvements achieved in tracking results after this integration, for an intelligent vehicle application

Obstacle Detection Based on Fusion Between Stereovision and 2D Laser Scanner

International audienceObstacle detection is an essential task for mobile robots. This subject has been investigated for many years by researchers and a lot of obstacle detection systems have been proposed so far. Yet designing an accurate and totally robust and reliable system remains a challenging task, above all in outdoor environments. Thus, the purpose of this chapter is to present new techniques and tools to design an accurate, robust and reliable obstacle detection system in outdoor environments based on a minimal number of sensors. So far, experiments and assessments of already developed systems show that using a single sensor is not enough to meet the requirements: at least two complementary sensors are needed. In this chapter a stereovision sensor and a 2D laser scanner are considered

Fusion of Telemetric and Visual Data from Road Scenes with a Lexus Experimental Platform

International audienceFusion of telemetric and visual data from traffic scenes helps exploit synergies between different on-board sensors, which monitor the environment around the ego-vehicle. This paper outlines our approach to sensor data fusion, detection and tracking of objects in a dynamic environment. The approach uses a Bayesian Occupancy Filter to obtain a spatio-temporal grid representation of the traffic scene. We have implemented the approach on our experimental platform on a Lexus car. The data is obtained in traffic scenes typical of urban driving, with multiple road participants. The data fusion results in a model of the dynamic environment of the ego-vehicle. The model serves for the subsequent analysis and interpretation of the traffic scene to enable collision risk estimation for improving the safety of driving

Using obstacles and road pixels in the disparity-space computation of stereo-vision based occupancy grids

International audienceOccupancy grids have been used for a variety of applications in the field of robotics. These grids have typically been created based on data provided by range sensors such as laser or ultrasound. Current practice is to create the grids based on a probabilistic sensor model such as [1]. The use of stereo-vision to create occupancy grids is less common. This paper will detail a novel approach to compute occupancy grids, as applied to intelligent vehicles. Occupancy is initially computed directly in the stereoscopic sensor's disparity space, allowing the handling of occlusions in the observed area. It is also computationally efficient, since it uses the u-disparity approach to avoid processing a large point cloud. The occupancy calculation formally accounts for the detection of obstacles and the road in disparity space, as well as partial occlusions in the scene. In a second stage, this disparity-space occupancy grid is transformed into a Cartesian space occupancy grid to be used by subsequent applications. This transformation includes a filtering step to reduce discretization effects and explicitly account for the relation between range and uncertainty in stereoscopic data. In this paper, we present the method and show the results obtained with real road data

Integration of visual and depth information for vehicle detection

In this work an object class recognition method is presented. The method uses local image features and follows the part based detection approach. It fuses intensity and depth information in a probabilistic framework. The depth of each local feature is used to weigh the probability of finding the object at a given scale. To train the system for an object class only a database of annotated with bounding boxes images is required, thus automatizing the extension of the system to different object classes. We apply our method to the problem of detecting vehicles from a moving platform. The experiments with a dataset of stereo images in an urban environment show a significant improvement in performance when using both information modalities

A Three Resolution Framework for Reliable Road Obstacle Detection using Stereovision

International audienceMany approaches have been proposed for in-vehicle obstacle detection using stereovision. Unfortunately, computation cost is generally a limiting factor for all these methods, especially for systems using large base-lines, as they need to explore a wide range of disparities. Considering this point, we propose a reliable three resolution framework, designed for real time operation, even with high resolution images and a large baseline

Probabilistic Grid-based Collision Risk Prediction for Driving Application

International audienceIn the recent years, more and more modern cars have been equipped with perception capabilities. One of the key applications of such perception systems is the estimation of a risk of collision. This is necessary for both Advanced Driver Assistance Systems and Autonomous Navigation. Most approach for risk estimation propose to detect and track the dynamic objects in the scene. Then the risk is estimated as a Time To Collision (TTC) by projecting the object's trajectory in the future. In this paper, we propose a new grid-based approach for collision risk prediction, based on the Hybrid-Sampling Bayesian Occupancy Filter framework. The idea is to compute an estimation of the TTC for each cell of the grid, instead of reasoning on objects. This strategy avoids to solve the difficult problem of multi-objects detection and tracking and provides a probabilistic estimation of the risk associated to each TTC value. After promising initial results, we propose in this paper to evaluate the relevance of the method for real on-road applications, by using a real-time implementation of our method in an experimental vehicle

Using the disparity space to compute occupancy grids from stereo-vision

International audienceThe occupancy grid is a popular tool for probabilistic robotics, used for a variety of applications. Such grids are typically based on data from range sensors (e.g. laser, ultrasound), and the computation process is well known. The use of stereo-vision in this framework is less common, and typically treats the stereo sensor as a distance sensor, or fails to account for the uncertainties specific to vision. In this paper, we propose a novel approach to compute occupancy grids from stereo-vision, for the purpose of intelligent vehicles. Occupancy is initially computed directly in the stereoscopic sensor's disparity space, using the sensor's pixel-wise precision during the computation process and allowing the handling of occlusions in the observed area. It is also computationally efficient, since it uses the u-disparity approach to avoid processing a large point cloud. In a second stage, this disparity-space occupancy is transformed into a Cartesian space occupancy grid to be used by subsequent applications. In this paper, we present the method and show results obtained with real road data, comparing this approach with others