{"content":[{"text":"Applying Attentional Mechanisms to Bi-modal 3D Laser Data","bbox":{"left":0.182695,"top":0.12172,"width":0.6349989999999999,"height":0.02413499999999999}},{"text":"Simone Frintrop, Erich Rome, Andreas N¨uchter, Hartmut Surmann \n Fraunhofer Institut f¨ur Autonome Intelligente Systeme \n Schloss Birlinghoven \n 53754 Sankt Augustin, Germany \n {ﬁrstname.surname}@ais.fraunhofer.de \n http://www.ais.fraunhofer.de","bbox":{"left":0.230485,"top":0.170018,"width":0.5394369999999999,"height":0.102518}},{"text":"Abstract","bbox":{"left":0.24537,"top":0.302301,"width":0.074765,"height":0.02011200000000002}},{"text":"In this paper we present experimental results on a novel \n application of visual attention mechanisms for the selection \n of points of interest in an arbitrary scene. The imaging sen- \n sor used is a multi-modal 3D laser scanner. In a single 3D \n scan pass, it is capable of providing range data as well as \n a gray-scale intensity image. The scanner is mounted on \n top of an autonomous mobile robot and serves control pur- \n poses. We present results achieved by applying the visual \n attention system of Itti et al. [8] to recorded scans of in- \n door and outdoor scenes. The vast majority of the primary \n attended locations pointed to scene objects of potential in- \n terest for navigation and object detection tasks. Moreover, \n both sensor modalities complement each other, resulting in \n a greater variety of points of interest than one modality \n alone can provide.","bbox":{"left":0.084222,"top":0.33487,"width":0.39714,"height":0.214978}},{"text":"ject recognition modules and the information about found \n objects can help to accomplish robot control tasks. \n This model, like many others, includes no depth feature, \n although in robotic applications depth is often employed for \n object detection tasks. Objects usually have range disconti- \n nuities at their borders which can help to detect them. Mod- \n els comprising depth as a feature typically use stereo vision \n to compute it [10, 2]. But stereo vision is computationally \n expensive, and only a fraction of the image pixels contribute \n to the computed 3D point clouds. \n As an alternative, 3D laser scanners are a class of sensors \n suitable for the fast acquisition of precise and dense depth or \n range information. The multi-modal 3D laser scanner used \n for our work [12] provides the technical means to acquire \n both range data as well as an intensity image of a scene in a \n single 3D scan pass. Since the data from the different sen- \n sor modalities result from the same measurement, we know \n exactly which remission or intensity value belongs to which \n range data. There is no need to establish correspondence by \n complex algorithms. \n Such a multi-sensor offers new algorithmic possibilities. \n It is to be expected that range data and remission values \n complement each other, providing some redundancy that \n can be exploited. Contrasts in range and in intensity need \n not necessarily correspond for one object. That is, an object \n producing the same intensity like its background may not \n be detected in a gray-scale image, but probably in the range \n data. On the other hand a ﬂat object on a ﬂat background – \n e.g. a poster on a wall or a letter on a desk – that could be \n clearly distinguished in an intensity image, may be too ﬂat \n to be detected in the range data. \n In this paper we use the data from the 3D laser scanner as \n input to the attentional model by generating a special input \n image by combining range and intensity data in a suitable \n way. We show the applicability of the laser data for atten- \n tional mechanisms and compare these results to the ones \n from corresponding camera images. For our experiments, \n we used recorded scan data from indoor and outdoor scenes.","bbox":{"left":0.519094,"top":0.305138,"width":0.39714399999999994,"height":0.544172}},{"text":"1. Introduction","bbox":{"left":0.084222,"top":0.581592,"width":0.12914399999999998,"height":0.02011200000000002}},{"text":"Common tasks in the control of autonomous mobile ro- \n bots are collision avoidance, navigation, and the manipula- \n tion of objects. In order to execute these tasks correctly, the \n robot needs to detect objects and free space in its environ- \n ment fast and reliably. One method to ﬁnd potential points \n of interest in the environment is to model human visual at- \n tention. \n In human vision, attention helps to identify relevant data \n and so to efﬁciently select information from the broad sen- \n sory input. These effects are even more desired in com- \n putational applications like image processing. Our work is \n based on the model of visual attention by Itti et al. [8]. In \n this model, different features like intensity, color and ori- \n entation are evaluated in parallel and fused into a single \n saliency map that topographically codes salient locations in \n a visual scene. These locations can be analyzed later by ob-","bbox":{"left":0.084222,"top":0.618786,"width":0.397141,"height":0.23052400000000006}}],"images_bbox":[],"images_bbox_not_text_overlap":[]}