Qadeer Baig

Online localization and mapping with moving objects detection in dynamic outdoor environments

In this paper, we present a real-time algorithm for online simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) in dynamic outdoor environments from a moving vehicle equipped with laser sensor and odometry. To correct vehicle location from odometry we introduce a new fast implementation of incremental scan matching method that can work reliably in dynamic outdoor environments. After a good vehicle location is estimated, the surrounding map is updated incrementally and moving objects are detected without a priori knowledge of the targets. Detected moving objects are finally tracked using Global Nearest Neighborhood (GNN) method. The experimental results on dataset collected from INTERSAFE-2 demonstrator for typical scenario show the effectiveness of this technique.

Improving moving objects tracking using road model for laser data

In this paper we have presented a fast algorithm to detect road borders from laser data. Two local search windows, one on right side of the host vehicle and the other on left, are moved right and left respectively from the current position of vehicle in map. A score function is evaluated to know the presence or absence of the road border in current search window. We have used the detected road border information to reduce false alarms in our previous work on DATMO (detection and tracking of moving objects). We also show how these information can be used to infer drivable area and the presence of intersections on the road. Results on data sets obtained from real demonstrator vehicles show that this technique can be successfully applied in real time.

Low level data fusion of laser and monocular color camera using occupancy grid framework

In this paper we have developed a technique for low level data fusion between laser and monocular color camera using occupancy grid framework in the context of internal representation of external environment for object detection. Based on a small variant of background subtraction technique we construct an occupancy grid for camera and fuse it with the one constructed for laser to get a combined view. The results obtained using Cycab simulator prepared by INRIA show the effectiveness of our technique.

Interacting multiple models based classification of moving objects

In this paper, we present an approach performing object behavior classification embedded in a complex and efficient perception method. This method, applied in dynamic outdoor environments using a moving vehicle equipped with a laser scanner, is composed of a local simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO). While the SLAM is performed by an implementation of incremental scan matching method, the tracking if performed by a Multiple Hypothesis Tracker (MHT) coupled with an adaptive Interacting Multiple Models Filter (IMM). The classification process takes place in the filtering stage and is based on one of the key parameters of the IMM filter which is the Transition Probability Matrix (TPM) modeling objects motion transitions. It permits to automatically classify object behavior and to reuse the classification output to enhance the prediction step in the filtering process. The experimental results on datasets collected from a Daimler Mercedes demonstrator in the framework of the European Project PReVENT-ProFusion2 demonstrate the capacity of the proposed algorithm.

A Generic Architecture for Dynamic Outdoor Environment

In this paper, we present a generic architecture for perception of an intelligent vehicle in dynamic outdoor environment. This architecture is composed of two levels: a first level dedicated to real-time local simultaneous localization and mapping (SLAM) and a second one is dedicated to detection and tracking of moving objects (DATMO). The experimental results on datasets collected from different scenarios such as: urban streets, country roads and highways demonstrate the efficiency of the proposed algorithm on a Daimler Mercedes demonstrator in the framework of the European Project PReVENT-ProFusion2 and on a Volkswagen Demonstrator in the framework of the European Project Intersafe2.

An Occupancy Grid Based Architecture for ADAS

Perceiving or understanding the environment surrounding a vehicle is a very important step in advanced driving assistance systems (ADAS). The task involves both Simultaneous Localization and Mapping (SLAM) and Detection and Tracking of Moving Objects (DATMO). In this context, we have developed a generic architecture based on occupancy grid to solve SLAM and DATMO in dynamic outdoor environments. In this paper, we give an overview of this architecture and results obtained in different European projects: PReVENT-ProFusion2, INTERSAFE-2 and Interactive.