Olivier Aycard

Online Localization and Mapping with Moving Object Tracking 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 datasets collected from different scenarios such as: urban streets, country roads and highways demonstrate the efficiency of the proposed algorithm.

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.

Laser-based detection and tracking moving objects using data-driven Markov chain Monte Carlo

We present a method of simultaneous detection and tracking moving objects from a moving vehicle equipped with a single layer laser scanner. A model-based approach is introduced to interpret the laser measurement sequence by hypotheses of moving object trajectories over a sliding window of time. Knowledge of various aspects including object model, measurement model, motion model are integrated in one theoretically sound Bayesian framework. The data-driven Markov chain Monte Carlo (DDMCMC) technique is used to sample the solution space effectively to find the optimal solution. Experiments and results on real-life data of urban traffic show promising results.

Intentional motion on-line learning and prediction

Predicting motion of humans, animals and other objects which move according to internal plans is a challenging problem. Most existing approaches operate in two stages: (a) learning typical motion patterns by observing an environment and (b) predicting future motion on the basis of the learned patterns. In existing techniques, learning is performed off-line, hence, it is impossible to refine the existing knowledge on the basis of the new observations obtained during the prediction phase. We propose an approach which uses hidden Markov models (HMMs) to represent motion patterns. It is different from similar approaches because it is able to learn and predict in a concurrent fashion thanks to a novel approximate learning approach, based on the growing neural gas algorithm, which estimates both HMM parameters and structure. The found structure has the property of being a planar graph, thus enabling exact inference in linear time with respect to the number of states in the model. Our experiments demonstrate that the technique works in real-time, and is able to produce sound long-term predictions of people motion.

Place learning and recognition using hidden Markov models

In this paper, we propose a new method based on hidden Markov models to learn and recognize places in an indoor environment by a mobile robot. Hidden Markov models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (e.g. neural networks) are their capabilities to modelize noisy temporal signals of variable length. We show in this paper that this approach is well adapted for learning and recognition of places by a mobile robot. Results of experiments on a real robot with five distinctive places are given.

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.

Wavelet Occupancy Grids: a Method for Compact Map Building

This paper introduces the structure of wavelet occupancy grids (WavOGs) as a tool for storing occupancy grids in a compact way. We have shown that WavOGs provide a continuous semantics of occupancy through scaled spaces. In accordance with the theoretical properties of wavelets, our experiments have validated that WavOGs allow major memory gains. WavOG as a compact multi-scaled tool provides an efficient framework for the various algorithms that use OGs such as robot navigation, spatio-temporal classification or multiple target-tracking. In future works we plan to apply WavOGs to the monitoring of urban traffic over large areas.

Fusion between laser and stereo vision data for moving objects tracking in intersection like scenario

Using multiple sensors in the context of environment perception for autonomous vehicles is quite common these days. Perceived data from these sensors can be fused at different levels like: before object detection, after object detection and finally after tracking the moving objects. In this paper we detail our object detection level fusion between laser and stereo vision sensors as opposed to pre-detection or track level fusion. We use the output of our laser processing to get a list of objects with position and dynamic properties for each object. Similarly we use the stereo vision output of another team which consists of a list of detected objects with position and classification properties for each object. We use Bayesian fusion technique on objects of these two lists to get a new list of fused objects. This fused list of objects is further used in tracking phase to track moving objects in an intersection like scenario. The results obtained on data sets of INTERSAFE-2 demonstrator vehicle show that this fusion has improved data association and track management steps.

PUVAME - New French Approach for Vulnerable Road Users Safety

In France, about 33% of roads victims are VRU. In its 3rd framework, the French PREDIT includes VRU Safety. The PUVAME project was created to generate solutions to avoid collisions between VRU and bus in urban traffic. An important part of these collisions take place at intersection or bus stop. In this paper, we detail the hardware and software architecture designed and developed in the project. This solution is based on offboard cameras observing particular places (intersection and bus stop in our case) to detect and track VRU present in the environment. The position of the bus is also computed and a risk of collision between each VRU and the bus is determined. In case of high risk of collision, the bus driver is warned. The HMI to warn the bus driver is also described. Finally, some experimental results are presented

Mobile robot localization in dynamic environments using places recognition

We present a new method to localize a mobile robot in dynamic environments. This method is based on place recognition, and a match between places recognized and the sequence of places that the mobile robot is able to see during a run from an initial place to an ending place. Our method gives a coarse idea of the robots position and orientation. Moreover, the robot can determine the actual state of places (i.e. open doors, closed doors).