Julien Burlet

Grid-based localization and local mapping with moving object detection and tracking

We present a real-time algorithm for simultaneous localization and local mapping (local SLAM) with detection and tracking of moving objects (DATMO) in dynamic outdoor environments from a moving vehicle equipped with a laser scanner, short-range radars and odometry. To correct the vehicle odometry we introduce a new fast implementation of incremental scan matching method that can work reliably in dynamic outdoor environments. After obtaining a good vehicle localization, the map surrounding of the vehicle is updated incrementally and moving objects are detected without a priori knowledge of the targets. Detected moving objects are finally tracked by a Multiple Hypothesis Tracker (MHT) coupled with an adaptive Interacting Multiple Model (IMM) filter. The experimental results on datasets collected from different scenarios such as: urban streets, country roads and highways demonstrate the efficiency of the proposed algorithm.

Results of a Precrash Application Based on Laser Scanner and Short-Range Radars

In this paper, we present a vehicle safety application based on data gathered by a laser scanner and two short-range radars that recognize unavoidable collisions with stationary objects before they take place to trigger restraint systems. Two different software modules that perform the processing of raw data and deliver a description of the vehicles environment are compared. A comprehensive experimental evaluation based on relevant crash and noncrash scenarios is presented.

Grid-based localization and online mapping with moving objects detection and tracking: new results

In this paper, we present a real-time algorithm for local simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) in dynamic outdoor environments from a moving vehicle equipped with a laser scanner. 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 by a multiple hypothesis tracker (MHT) coupled with an adaptive IMM (interacting multiple models) filter. 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.

High Level Sensor Data Fusion Approaches For Object Recognition In Road Environment

Application of high level fusion approaches demonstrate a sequence of significant advantages in multi sensor data fusion and automotive safety fusion systems are no exception to this. High level fusion can be applied to automotive sensor networks with complementary or/and redundant field of views. The advantage of this approach is that it ensures system modularity and allows benchmarking, as it does not permit feedbacks and loops inside the processing. In this paper two specific high level data fusion approaches are described including a brief architectural and algorithmic presentation. These approaches differ mainly in their data association part: (a) track level fusion approach solves it with the point to point association with emphasis on object continuity and multidimensional assignment, and (b) grid based fusion approach that proposes a generic way to model the environment and to perform sensor data fusion. The test case for these approaches is a multi sensor equipped PReVENT/ProFusion2 truck demonstrator vehicle.

Robust motion planning using Markov decision processes and quadtree decomposition

To reach a given goal, a mobile robot first computes a motion plan (if a sequence of actions that will take it to its goal), and then executes it Markov decision processes (MDPs) have been successfully used to solve these two problems. Their main advantage is that they provide a theoretical framework to deal with the uncertainties related to the robots motor and perceptive actions during both planning and execution stages. This paper describes a MDP-based planning method that uses a hierarchic representation of the robots state space (based on a quadtree decomposition of the environment). Besides, the actions used better integrate the kinematic constraints of a wheeled mobile robot. These two features yield a motion planner more efficient and better suited to plan robust motion strategies.

Results of a precrash application based on Laserscanner and short range radars

In this paper, we present a vehicle safety application based on data gathered by a laserscanner and two short range radars that recognizes unavoidable collisions with stationary objects before they take place in order to trigger restraint systems. Two different software modules are compared that perform the processing of raw data and deliver a description of the vehiclepsilas environment. A comprehensive experimental evaluation based on relevant crash and non-crash scenarios is presented.

Pedestrian Tracking in Car Parks : An Adaptive Interacting Multiple Models Based Filtering Method

To address perception problems we must be able to track dynamics targets of the environment. An important issue of tracking is filtering problem in which estimates of the targets state are computed while observations are progressively received. This paper presents an adaptive interacting multiple models (IMM) based filtering method. Interacting multiple models have been successfully applied to many applications as they allow, using several filters in parallel, to deal with the uncertainty on motion model, a critical component of filtering. Indeed targets can rapidly change their motion over a lapse of time. This is the case of pedestrians for which it is difficult to define a unique motion model which matches all their possible displacements. Nevertheless, the transition probability matrix (TPM) which models the interaction between different filters in an IMM is in currently defined a priori or needs an important amount of tuning to be used efficiently. In this paper, we put forward a method which automatically adapts online the TPM. The TPM adaptation using on-line data significantly improves the effectiveness of IMM filtering and so better target estimates are obtained. To validate our work we applied our method to pedestrian tracking in car parks on a real platform

Adaptive Interacting Multiple Models applied on pedestrian tracking in car parks

To address perception problems we must be able to track dynamics targets of the environment. An important issue of tracking is filtering problem in which estimates of the targets state are computed while observations are progressively received. This paper presents an adaptive interacting multiple models (IMM) based filtering method. Interacting multiple models have been successfully applied to many applications as they allow, using several filters in parallel, to deal with the uncertainty on motion model, a critical component of filtering. Indeed targets can rapidly change their motion over a lapse of time. This is the case of pedestrians for which it is difficult to define an unique motion model which matches all their possible displacements. Nevertheless, the transition probability matrix (TPM) which models the interaction between different filters in an IMM is in currently defined a priori or needs an important amount of tuning to be used efficiently. In this paper, we put forward a method which automatically adapts online the TPM. The TPM adaptation using on-line data significantly improves the effectiveness of IMM filtering and so better target estimates are obtained. To validate our work we applied our method to pedestrian tracking in car parks on a real platform

Frontal object perception using radar and mono-vision

In this paper, we detail a complete software architecture of a key task that an intelligent vehicle has to deal with: frontal object perception. This task is solved by processing raw data of a radar and a mono-camera to detect and track moving objects. Data sets obtained from highways, country roads and urban areas were used to test the proposed method. Several experiments were conducted to show that the proposed method obtains a better environment representation, i.e., reduces the false alarms and missed detections from individual sensor evidence.

Robust navigation using Markov models

To reach a given goal, a mobile robot first computes a motion plan (i.e. a sequence of actions that takes it to its goal), and then executes it. Markov decision processes (MDPs) have been successfully used to solve these two problems. Their main advantage is that they provide a theoretical framework to deal with the uncertainties related to the robots motor and perceptive actions during both planning and execution stages. While a previous paper addressed the motion planning stage, this paper deals with execution stage. It describes an approach based on Markov localization and focuses on experimental aspects, in particular, the learning of the transition function (that encodes the uncertainties related to the robot actions) and the sensor model. Experimental results carry out with a real robot demonstrate the robustness of the whole navigation approach.