Learning to automatically detect features for mobile robots using second-order Hidden Markov Models

In this paper, we propose a new method based on Hidden Markov Models to interpret temporal sequences of sensor data from mobile robots to automatically detect features. Hidden Markov Models have been used for a long time in pattern recognition, especially in speech recognition. Their main advantages over other methods (such as neural networks) are their ability to model noisy temporal signals of variable length. We show in this paper that this approach is well suited for interpretation of temporal sequences of mobile-robot sensor data. We present two distinct experiments and results: the first one in an indoor environment where a mobile robot learns to detect features like open doors or T-intersections, the second one in an outdoor environment where a different mobile robot has to identify situations like climbing a hill or crossing a rock.Comment: 200

The use of Iscamtho by children in white city-Jabavu, Soweto: slang and language contact in an African urban context

Includes bibliographical references.The work presented in this thesis relies on language recordings gathered during thirty months of fieldwork in White City-Jabavu, Soweto. The data was collected from children between the ages of two and nine, following anthropological participant observation, and through the use of an audio recorder. Strong attention was given to the sociolinguistics and structure of the language collected. This thesis is interested in issues of slang use among children and language contact, as part of the larger field of tsotsitaal studies. It is interested in: sociolinguistic issues of registers, slang, and style; and linguistic issues regarding the structural output of language contact. The main questions answered in the thesis concern whether children in White City use the local tsotsitaal, known as Iscamtho; and what particular kind of mixed variety supports their use of Iscamtho. Particularly, I focus on the prediction of the Matrix Language Frame model (Myers-Scotton 2002) regarding universal constraints on the output of language contact. This model was used previously to analyse Iscamtho use in Soweto. Using methodologies from three different disciplinary fields (anthropology, sociolinguistics, and linguistics) as well as four different analytic perspectives (participatory, statistical, conversational, and structural), I offer a thorough sociolinguistic and linguistic description of the children's language. I demonstrate that the universal constraints previously identified do not apply to a significant part of the children's speech, due to stylistic and multilingual practices in the local linguistic community. I further demonstrate that style, slang, and deliberate variations in language, can produce some unpredictable and yet stable structural output of language contact, which contradicts the main hypotheses of universal natural constraints over this output formulated by the Matrix Language Frame model

Multiple Sensor Fusion and Classification for Moving Object Detection and Tracking

International audience—The accurate detection and classification of moving objects is a critical aspect of Advanced Driver Assistance Systems (ADAS). We believe that by including the objects classification from multiple sensors detections as a key component of the object's representation and the perception process, we can improve the perceived model of the environment. First, we define a composite object representation to include class information in the core object's description. Second , we propose a complete perception fusion architecture based on the Evidential framework to solve the Detection and Tracking of Moving Objects (DATMO) problem by integrating the composite representation and uncertainty management. Finally, we integrate our fusion approach in a real-time application inside a vehicle demonstrator from the interactIVe IP European project which includes three main sensors: radar, lidar and camera. We test our fusion approach using real data from different driving scenarios and focusing on four objects of interest: pedestrian, bike, car and truck

Object Perception for Intelligent Vehicle Applications: A Multi-Sensor Fusion Approach

International audienceThe paper addresses the problem of object perception for intelligent vehicle applications with main tasks of detection, tracking and classification of obstacles where multiple sensors (i.e.: lidar, camera and radar) are used. New algorithms for raw sensor data processing and sensor data fusion are introduced making the most information from all sensors in order to provide a more reliable and accurate information about objects in the vehicle environment. The proposed object perception module is implemented and tested on a demonstrator car in real-life traffics and evaluation results are presented

Audiovisual data fusion for successive speakers tracking

International audienceIn this paper, a human speaker tracking method on audio and video data is presented. It is applied to con- versation tracking with a robot. Audiovisual data fusion is performed in a two-steps process. Detection is performed independently on each modality: face detection based on skin color on video data and sound source localization based on the time delay of arrival on audio data. The results of those detection processes are then fused thanks to an adaptation of bayesian filter to detect the speaker. The robot is able to detect the face of the talking person and to detect a new speaker in a conversation

Fusion at Detection Level for Frontal Object Perception

International audienceIntelligent vehicle perception involves the correct detection and tracking of moving objects. Taking into account all the possible information at early levels of the perception task can improve the final model of the environment. In this paper, we present an evidential fusion framework to represent and combine evidence from multiple lists of sensor detections. Our fusion framework considers the position, shape and appearance information to represent, associate and combine sensor detections. Although our approach takes place at detection level, we propose a general architecture to include it as a part of a whole perception solution. Several experiments were conducted using real data from a vehicle demonstrator equipped with three main sensors: lidar, radar and camera. The obtained results show improvements regarding the reduction of false detections and mis-classifications of moving objects

Fusion at Detection Level for Frontal Object Perception

International audienceIntelligent vehicle perception involves the correct detection and tracking of moving objects. Taking into account all the possible information at early levels of the perception task can improve the final model of the environment. In this paper, we present an evidential fusion framework to represent and combine evidence from multiple lists of sensor detections. Our fusion framework considers the position, shape and appearance information to represent, associate and combine sensor detections. Although our approach takes place at detection level, we propose a general architecture to include it as a part of a whole perception solution. Several experiments were conducted using real data from a vehicle demonstrator equipped with three main sensors: lidar, radar and camera. The obtained results show improvements regarding the reduction of false detections and mis-classifications of moving objects

Fusion Framework for Moving-Object Classification

International audiencePerceiving the environment is a fundamental task for Advance Driver Assistant Systems. While simultaneous localization and mapping represents the static part of the environment, detection and tracking of moving objects aims at identifying the dynamic part. Knowing the class of the moving objects surrounding the vehicle is a very useful information to correctly reason, decide and act according to each class of object, e.g. car, truck, pedestrian, bike, etc. Active and passive sensors provide useful information to classify certain kind of objects, but perform poorly for others. In this paper we present a generic fusion framework based on Dempster-Shafer theory to represent and combine evidence from several sources. We apply the proposed method to the problem of moving object classification. The method combines information from several lists of moving objects provided by different sensor-based object detectors. The fusion approach includes uncertainty from the reliability of the sensors and their precision to classify specific types of objects. The proposed approach takes into account the instantaneous information at current time and combines it with fused information from previous times. Several experiments were conducted in highway and urban scenarios using a vehicle demonstrator from the interactIVe European project. The obtained results show improvements in the combined classification compared with individual class hypothesis from the individual detector modules

Geometric and Bayesian models for safe navigation in dynamic environments

Autonomous navigation in open and dynamic environments is an important challenge, requiring to solve several difficult research problems located on the cutting edge of the state of the art. Basically, these problems may be classified into three main categories: (a) SLAM in dynamic environments; (b) detection, characterization, and behavior prediction of the potential moving obstacles; and (c) online motion planning and safe navigation decision based on world state predictions. This paper addresses some aspects of these problems and presents our latest approaches and results. The solutions we have implemented are mainly based on the followings paradigms: multiscale world representation of static obstacles based on the wavelet occupancy grid; adaptative clustering for moving obstacle detection inspired on Kohonen networks and the growing neural gas algorithm; and characterization and motion prediction of the observed moving entities using Hidden Markov Models coupled with a novel algorithm for structure and parameter learnin

An Evidential Filter for Indoor Navigation of a Mobile Robot in Dynamic Environment

International audienceRobots are destined to live with humans and perform tasks for them. In order to do that, an adapted representation of the world including human detection is required. Evidential grids enable the robot to handle partial information and ignorance, which can be useful in various situations. This paper deals with an audiovisual perception scheme of a robot in indoor environment (apartment, house..). As the robot moves, it must take into account its environment and the humans in presence. This article presents the key-stages of the multimodal fusion: an evidential grid is built from each modality using a modified Dempster combination, and a temporal fusion is made using an evidential filter based on an adapted version of the generalized bayesian theorem. This enables the robot to keep track of the state of its environment. A decision can then be made on the next move of the robot depending on the robot's mission and the extracted information. The system is tested on a simulated environment under realistic conditions