Acquiring High Dynamic Range (HDR) photos from several images, with an active shutter providing different exposures (sensor integration periods), has been widely commercialised in photography for static camera positions. In the case of a mobile video sensor (as is the case in robotics), this problem is more difficult due to real-time motion of the sensor which transforms the perspective between the acquired images. HDR approaches for a set of images from different perspectives have therefore been significantly overlooked since this would require sophisticated dense mapping approaches to eliminate the motion component. Recent dense visual SLAM (Simultaneous Localization And Mapping) approaches provide this framework, however, few works have attempted to perform HDR visual SLAM. Current approaches are thus highly dependant on illumination conditions and camera shutter settings. In this paper a new approach is proposed that enables 3D HDR environment maps to be acquired actively from a dynamic set of images in real-time. The 6 DOF pose, the dense scene structure and the HDR texture map will be estimated simultaneously with the objective of maximising the dynamic range. This will allow to obtain a radiance map of the scene by fusing a real-time stream of low dynamic range images (LDR) into a graph of HDR key-frame images. In particular, a method is proposed to actively control the shutter based on information theory to optimise the information content of the 3D HDR environment map for RGB-D sensors. As will be shown in the results, a 3D HDR environment map allows mapping (visual SLAM) to take actively advantage of varying luminosity in different parts of the scene.

Acquiring High Dynamic Range (HDR) light-fields from several images with different exposures (sensor integration periods) has been widely considered for static camera positions. In this paper a new approach is proposed that enables 3D HDR environment maps to be acquired directly from a dynamic set of images in real-time. In particular a method will be proposed to use an RGB-D camera as a dynamic light-field sensor, based on a dense real-time 3D tracking and mapping approach, that avoids the need for a light-probe or the observation of reflective surfaces. The 6dof pose and dense scene structure will be estimated simultaneously with the observed dynamic range so as to compute the radiance map of the scene and fuse a stream of low dynamic range images (LDR) into an HDR image. This will then be used to create an arbitrary number of virtual omni-directional light-probes that will be placed at the positions where virtual augmented objects will be rendered. In addition, a solution is provided for the problem of automatic shutter variations in visual SLAM. Augmented reality results are provided which demonstrate real-time 3D HDR mapping, virtual light-probe synthesis and light source detection for rendering reflective objects with shadows seamlessly with the real video stream in real-time.

In this paper we propose a modified version of the classical explicit snake (active contour) approach for the segmentation of irregular object shape in video images. In the classical approach the external evolution force is computed using the gradient of the image or the region information. In our original proposed method the external force is computed using the region information with two windows around the contour point. This new approach improves the speed of convergence and also the quality of segmentation especially for irregular objects shape(objects with sharp corners, deep concave/convex shape). As the contour points are attracted to the discontinuity of form, the modified snake is called “comer tracker snake”.

The paper presents work on autonomous tracking of boundaries between distinct benthic regions using an AUV equipped of a mechanically scanning profiler sonar. By exploiting sonar scans of the region below the robot, a classical control loop is closed around the sonar data, using a feedback signal that is robust with respect to classification "noise." Results obtained in a real experiment of tracking a boundary between sand and Posidonia are presented.

The use of mobile robots, still limited to specific applications, can be applied to service robotics such as assistance for the disabled, if efficient yet low-cost perception systems are available. A multiaural and multimodal ultrasonic device seems to be an appropriate means for achieving such a difficult compromise. In a multimodal perception system, each operating mode is suited to a specific task. The paper presents three different main working modes based on the needs of a robot for obstacle avoidance, localization and target tracking. In order to take into account either a sensor anomaly or specific constraints imposed by the robot, variations of the main modes are proposed under the name reduced service mode (RSM). Related to the main mode which is optimal a RSM establishes a new compromise between performance and response time. The paper focuses on pattern recognition -notably for target tracking- using classification by neural network but above all by statistical methods. Results show that the ultrasonic received signal allows the extraction of complex objects from the environment with a good recognition percentage. Ultrasonic technology can be used for functions different from the only obstacle avoidance.

This study investigates the processing of sonar signal using statistical methods (quadratic and linear) for differentiation of commonly encountered features in indoor robot environments. Statistical method can differentiate two targets (Plane and Corner) with higher accuracy, improving on previously reported methods. It achieves this by exploiting identifying echo features: the differential frequency, length, amplitude and time-of-flight (TOF) characteristics of these targets. The features extraction is also studied and the reduction of parameters is obtained with a SBS (Sequential Backward Selection) approach. The results indicate that the amplitude information is more crucial than other features. The study suggests a wider use of statistical methods and amplitude information in sonar-based mobile robotics.