Simultaneous Parameter Calibration, Localization, and Mapping

The calibration parameters of a mobile robot play a substantial role in navigation tasks. Often these parameters are subject to variations that depend either on changes in the environment or on the load of the robot. In this paper, we propose an approach to simultaneously estimate a map of the environment, the position of the on-board sensors of the robot, and its kinematic parameters. Our method requires no prior knowledge about the environment and relies only on a rough initial guess of the parameters of the platform. The proposed approach estimates the parameters online and it is able to adapt to non-stationary changes of the configuration. We tested our approach in simulated environments and on a wide range of real-world data using different types of robotic platforms. (C) 2012 Taylor & Francis and The Robotics Society of Japa

3D modeling of indoor environments by a mobile platform with a laser scanner and panoramic camera

One major challenge of 3DTV is content acquisition. Here, we present a method to acquire a realistic, visually convincing D model of indoor environments based on a mobile platform that is equipped with a laser range scanner and a panoramic camera. The data of the 2D laser scans are used to solve the simultaneous lo- calization and mapping problem and to extract walls. Textures for walls and floor are built from the images of a calibrated panoramic camera. Multiresolution blending is used to hide seams in the gen- erated textures. The scene is further enriched by 3D-geometry cal- culated from a graph cut stereo technique. We present experimental results from a moderately large real environment.

Multi sensor fusion of camera and 3D laser range finder for object recognition

Proceedings of: 2010 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), September 5-7, 2010, Salt Lake City, USAThis paper proposes multi sensor fusion based on an effective calibration method for a perception system designed for mobile robots and intended for later object recognition. The perception system consists of a camera and a three-dimensional laser range finder. The three-dimensional laser range finder is based on a two-dimensional laser scanner and a pan-tilt unit as a moving platform. The calibration permits the coalescence of the two most important sensors for three-dimensional environment perception, namely a laser scanner and a camera. Both sensors permit multi sensor fusion consisting of color and depth information. The calibration process based upon a specific calibration pattern is used to define the extrinsic parameters and calculate the transformation between a laser range finder and a camera. The found transformation assigns an exact position and the color information to each point of the surroundings. As a result, the advantages of both sensors can be combined. The resulting structure consists of colored unorganized point clouds. The achieved results can be visualized with OpenGL and used for surface reconstruction. This way, typical robotic tasks like object recognition, grasp calculation or handling of objects can be realized. The results of our experiments are presented in this paper.European Community's Seventh Framework Progra

Real-Time fusion of visual images and laser data images for safe navigation in outdoor environments

[EN]In recent years, two dimensional laser range finders mounted on vehicles is becoming a fruitful solution to achieve safety and environment recognition requirements (Keicher & Seufert, 2000), (Stentz et al., 2002), (DARPA, 2007). They provide real-time accurate range measurements in large angular fields at a fixed height above the ground plane, and enable robots and vehicles to perform more confidently a variety of tasks by fusing images from visual cameras with range data (Baltzakis et al., 2003). Lasers have normally been used in industrial surveillance applications to detect unexpected objects and persons in indoor environments. In the last decade, laser range finder are moving from indoor to outdoor rural and urban applications for 3D imaging (Yokota et al., 2004), vehicle guidance (Barawid et al., 2007), autonomous navigation (Garcia-Pérez et al., 2008), and objects recognition and classification (Lee & Ehsani, 2008), (Edan & Kondo, 2009), (Katz et al., 2010). Unlike industrial applications, which deal with simple, repetitive and well-defined objects, cameralaser systems on board off-road vehicles require advanced real-time techniques and algorithms to deal with dynamic unexpected objects. Natural environments are complex and loosely structured with great differences among consecutive scenes and scenarios. Vision systems still present severe drawbacks, caused by lighting variability that depends on unpredictable weather conditions. Camera-laser objects feature fusion and classification is still a challenge within the paradigm of artificial perception and mobile robotics in outdoor environments with the presence of dust, dirty, rain, and extreme temperature and humidity. Real time relevant objects perception, task driven, is a main issue for subsequent actions decision in safe unmanned navigation. In comparison with industrial automation systems, the precision required in objects location is usually low, as it is the speed of most rural vehicles that operate in bounded and low structured outdoor environments. To this aim, current work is focused on the development of algorithms and strategies for fusing 2D laser data and visual images, to accomplish real-time detection and classification of unexpected objects close to the vehicle, to guarantee safe navigation. Next, class information can be integrated within the global navigation architecture, in control modules, such as, stop, obstacle avoidance, tracking or mapping.Section 2 includes a description of the commercial vehicle, robot-tractor DEDALO and the vision systems on board. Section 3 addresses some drawbacks in outdoor perception. Section 4 analyses the proposed laser data and visual images fusion method, focused in the reduction of the visual image area to the region of interest wherein objects are detected by the laser. Two methods of segmentation are described in Section 5, to extract the shorter area of the visual image (ROI) resulting from the fusion process. Section 6 displays the colour based classification results of the largest segmented object in the region of interest. Some conclusions are outlined in Section 7, and acknowledgements and references are displayed in Section 8 and Section 9.projects: CICYT- DPI-2006-14497 by the Science and Innovation Ministry, ROBOCITY2030 I y II: Service Robots-PRICIT-CAM-P-DPI-000176- 0505, and SEGVAUTO: Vehicle Safety-PRICIT-CAM-S2009-DPI-1509 by Madrid State Government.Peer reviewe

Viewfinder: final activity report

The VIEW-FINDER project (2006-2009) is an 'Advanced Robotics' project that seeks to apply a semi-autonomous robotic system to inspect ground safety in the event of a fire. Its primary aim is to gather data (visual and chemical) in order to assist rescue personnel. A base station combines the gathered information with information retrieved from off-site sources. The project addresses key issues related to map building and reconstruction, interfacing local command information with external sources, human-robot interfaces and semi-autonomous robot navigation. The VIEW-FINDER system is a semi-autonomous; the individual robot-sensors operate autonomously within the limits of the task assigned to them, that is, they will autonomously navigate through and inspect an area. Human operators monitor their operations and send high level task requests as well as low level commands through the interface to any nodes in the entire system. The human interface has to ensure the human supervisor and human interveners are provided a reduced but good and relevant overview of the ground and the robots and human rescue workers therein

Adaptive sensor-fusion of depth and color information for cognitive robotics

Proceedings of: 2011 IEEE International Conference on Robotics and Biomimetics (ROBIO), December 7-11, 2011, Phuket (Thailand)The presented work goes one step further than only combining data from different sensors. The corresponding points of an image and a 3D point cloud are determined through calibration. Color information is thereby assigned to every voxel in the overlapping area of a stereo camera system and a laser range finder. Then we analyze the image and search for the locations, which are especially susceptible to errors by both sensors. Depending on the ascertained situation, we try to correct or minimize errors. By analyzing and interpreting the images as well as removing errors we create an adaptive tool which improves multi-sensor fusion. This allows us to correct the fused data and to perfect the multi-modal sensor fusion or to predict the locations where the sensor information is vague or defective. The presented results demonstrate a clear improvement over standard procedures and show that other progress based on our work is possible.European Community's Seventh Framework Progra