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

Enhancing Mobile Object Classification Using Geo-referenced Maps and Evidential Grids

Evidential grids have recently shown interesting properties for mobile object perception. Evidential grids are a generalisation of Bayesian occupancy grids using Dempster- Shafer theory. In particular, these grids can handle efficiently partial information. The novelty of this article is to propose a perception scheme enhanced by geo-referenced maps used as an additional source of information, which is fused with a sensor grid. The paper presents the key stages of such a data fusion process. An adaptation of conjunctive combination rule is presented to refine the analysis of the conflicting information. The method uses temporal accumulation to make the distinction between stationary and mobile objects, and applies contextual discounting for modelling information obsolescence. As a result, the method is able to better characterise the occupied cells by differentiating, for instance, moving objects, parked cars, urban infrastructure and buildings. Experiments carried out on real- world data illustrate the benefits of such an approach.Comment: 6 pp. arXiv admin note: substantial text overlap with arXiv:1207.101

Credibilist Simultaneous Localization and Mapping with a LIDAR

International audienceFrom the early beginning, the Simultaneous Localization And Mapping (SLAM) problem has been approached using a probabilistic background. A new solution based on the Transferable Belief Model (TBM) framework is proposed in this article. It appears that this representation of knowledge affords numerous advantages over the classic probabilistic ones and leads to particularly good performances (an average of 3.2% translation drift and 0.0040deg/m rotation drift), especially when it comes to crowded environment. By introducing the basic concepts of a Credibilist SLAM, this article aims at proving that the use of this new theoretical context opens a lot of perspectives for the SLAM community.Dès le départ, la problématique de localisation et cartographie simultanée (SLAM) a été approchée avec un contexte probabiliste. Une nouvelle solution basée sur les modèles de croyance transférable (TBM) est proposée dans cet article. Ce type de représentation de la connaissance s'avère avantageux en comparaison aux probabilités et conduit à de particulièrement bonnes performances (une moyenne de 3,2% en dérive en translation et de 0,0040 deg/m en dérive en rotation), spécialement pour les environnements encombrés. En introduisant les concepts de base d'un SLAM crédibiliste, cet article tente de prouver que l'utilisation de ce nouveau contexte théorique ouvre de nombreuses perspectives pour la communauté du SLA

Collaborative Grid Mapping for Moving Object Tracking Evaluation

Perception of other road users is a crucial task for intelligent vehicles. Perception systems can use on-board sensors only or be in cooperation with other vehicles or with roadside units. In any case, the performance of perception systems has to be evaluated against ground-truth data, which is a particularly tedious task and requires numerous manual operations. In this article, we propose a novel semi-automatic method for pseudo ground-truth estimation. The principle consists in carrying out experiments with several vehicles equipped with LiDAR sensors and with fixed perception systems located at the roadside in order to collaboratively build reference dynamic data. The method is based on grid mapping and in particular on the elaboration of a background map that holds relevant information that remains valid during a whole dataset sequence. Data from all agents is converted in time-stamped observations grids. A data fusion method that manages uncertainties combines the background map with observations to produce dynamic reference information at each instant. Several datasets have been acquired with three experimental vehicles and a roadside unit. An evaluation of this method is finally provided in comparison to a handmade ground truth

Radar-based Dynamic Occupancy Grid Mapping and Object Detection

Environment modeling utilizing sensor data fusion and object tracking is crucial for safe automated driving. In recent years, the classical occupancy grid map approach, which assumes a static environment, has been extended to dynamic occupancy grid maps, which maintain the possibility of a low-level data fusion while also estimating the position and velocity distribution of the dynamic local environment. This paper presents the further development of a previous approach. To the best of the author's knowledge, there is no publication about dynamic occupancy grid mapping with subsequent analysis based only on radar data. Therefore in this work, the data of multiple radar sensors are fused, and a grid-based object tracking and mapping method is applied. Subsequently, the clustering of dynamic areas provides high-level object information. For comparison, also a lidar-based method is developed. The approach is evaluated qualitatively and quantitatively with real-world data from a moving vehicle in urban environments. The evaluation illustrates the advantages of the radar-based dynamic occupancy grid map, considering different comparison metrics.Comment: Accepted to be published as part of the 23rd IEEE International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece, September 20-23, 202

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

Improved dynamic object detection within evidential grids framework

International audienceThe deployment of autonomous robots/vehicles is increasing in several domains. To perform tasks properly, a robot must have a good perception about its environment while detecting dynamic obstacles. Recently, evidential grids have attracted more interest for environment perception since they permit more effective uncertainty handling. The latest studies on evidential grids relied on the use of thresholds for information management e.g. the use of a threshold, for the conflict characterized by the mass of empty set, in order to detect dynamic objects. Nevertheless, the mass of empty set alone is not consistent in some cases. Also, the thresholds used were chosen either arbitrary or tuned manually without any computational method. In this paper, first the conflict is composed of two parameters instead of mass of empty set alone, and dynamic objects detection is performed using a threshold on the evolution of this conflict pair. Secondly, the paper introduces a general threshold along with a mathematical demonstration to compute it which can be used in different dynamic object detection cases. A real-time experiment is performed using the RB1-BASE robot equipped with a RGB-D camera and a laser scanner

Multi-sensor based object detection in driving scenes

The work done in this internship consists in two main part. The first part is the design of an experimental platform to acquire data for testing and training. To design the experiments, onboard and onroad sensors have been considered. A calibration process has been conducted in order to integrated all the data from different sources. The second part was the use of a stereo system and a laser scanner to extract the free navigable space and to detect obstacles. This has been conducted through the use of an occupancy grid map representation

Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping

International audienceNowadays, many intelligent vehicles are equipped with various sensors to recognize their surrounding environment and to measure the motion or position of the vehicle. In addition, the number of intelligent vehicles equipped with a mobile Internet modem is increasing. Based on the sensors and Internet connection, the intelligent vehicles are able to share the sensor information with other vehicles via a cloud service. The sensor information sharing via the cloud service promises to improve the safe and efficient operation of the multiple intelligent vehicles. This paper presents a cloud update framework of occupancy grid maps for multiple intelligent vehicles in a large-scale environment. An evidential theory is applied to create the occupancy grid maps to address sensor disturbance such as measurement noise, occlusion and dynamic objects. Multiple vehicles equipped with LiDARs, motion sensors, and a low-cost GPS receiver create the evidential occupancy grid map (EOGM) for their passing trajectory based on GraphSLAM. A geodetic quad-tree tile system is applied to manage the EOGM, which provides a common tiling format to cover the large-scale environment. The created EOGM tiles are uploaded to EOGM cloud and merged with old EOGM tiles in the cloud using Dempster combination of evidential theory. Experiments were performed to evaluate the multiple EOGM mapping and the cloud update framework for large-scale road environment