Experimental Evaluation of Autonomous Driving Based on Visual Memory and Image Based Visual Servoing

International audienceIn this paper, the performance of a topological metric visual-path-following framework is investigated in different environments. The framework relies on a monocular camera as the only sensing modality. The path is represented as a series of refer- ence images such that each neighboring pair contains a number of common landmarks. Local 3-D geometries are reconstructed between the neighboring reference images to achieve fast feature prediction. This condition allows recovery from tracking failures. During navigation, the robot is controlled using image-based vi- sual servoing. The focus of this paper is on the results from a num- ber of experiments that were conducted in different environments, lighting conditions, and seasons. The experiments with a robot car show that the framework is robust to moving objects and moderate illumination changes. It is also shown that the system is capable of online path learning

A framework for scalable vision-only navigation

International audienceThis paper presents a monocular vision framework enabling feature-oriented appearance-based navigation in large outdoor environ- ments containing other moving ob jects. The framework is based on a hybrid topological-geometrical environment representation, constructed from a learning sequence acquired during a robot motion under hu- man control. The framework achieves the desired navigation functional- ity without requiring a global geometrical consistency of the underlying environment representation. The main advantages with respect to con- ventional alternatives are unlimited scalability, real-time mapping and effortless dealing with interconnected environments once the loops have been properly detected. The framework has been validated in demanding, cluttered and interconnected environments, under different imaging con- ditions. The experiments have been performed on many long sequences acquired from moving cars, as well as in real-time large-scale navigation trials relying exclusively on a single perspective camera. The obtained results imply that a globally consistent geometric environment model is not mandatory for successful vision-based outdoor navigation

Reduced sediment supply in a fast eroding landscape? A multi-proxy sediment budget of the upper Rhone basin, Central Alps

Alpine water and sediment supply influence the sediment budget of many important European fluvial systems such as the Rhine, Rhône and Po rivers. In the light of human induced climate change and landscape modification, it becomes increasingly important to understand the mechanisms of sediment production and supply in Alpine sediment systems. This study aims to investigate the modern sediment budget of the upper Rhône basin, one of the largest Alpine intramontane watersheds, located in the Central Alps of southwestern Switzerland. Major areas of sediment generation are fingerprinted by framework petrography, heavy mineral concentrations and bulk geochemistry. The relative contributions of the three major sources to the sediment of the trunk Rhône river are identified by compositional mixing modelling. Concentrations of the terrestrial cosmogenic nuclide 10Be measured in quartz separated from fluvial sediments provide spatially averaged denudation rates for selected tributary basins. Results from sediment fingerprinting and mixing modelling suggest that tributaries located in the North and the East of the catchment are generating most of the sediment transported by the Rhône river to its primary sedimentary sink in Lake Geneva. Despite having some of the highest denudation rates within the basin, tributaries located in the southern area of the Rhône basin are relatively underrepresented in the sediment budget of the Rhône river. These tributaries are severely affected by human activities, for example through sediment mining as well as water and sediment abstraction in large hydropower reservoirs. Together, these processes reduce the basin-wide sediment discharge by about 50%, thereby explaining most of the observed compositional pattern. In addition, there is evidence suggesting that large amounts of glaciogenic sediments are currently supplied by retreating glaciers. Glaciogenic material with its low 10Be concentrations can lead to a significant overestimation of denudation rates and thus limit the applicability of cosmogenic nuclide analysis in such glaciated settings

Using Multi-view Recognition and Meta-data Annotation to Guide a Robot's Attention

In the transition from industrial to service robotics, robots will have to deal with increasingly unpredictable and variable environments. We present a system that is able to recognize objects of a certain class in an image and to identify their parts for potential interactions. The method can recognize objects from arbitrary viewpoints and generalizes to instances that have never been observed during training, even if they are partially occluded and appear against cluttered backgrounds. Our approach builds on the implicit shape model of Leibe et al. We extend it to couple recognition to the provision of meta-dat

Outdoor visual path following experiments

International audienceIn this paper the performance of a topological- metric visual path following framework is investigated in different environments. The framework relies on a monocular camera as the only sensing modality. The path is represented as a series of reference images such that each neighboring pair contains a number of common landmarks. Local 3D geometries are reconstructed between the neighboring reference images in order to achieve fast feature prediction which allows the recovery from tracking failures. During navigation the robot is controlled using image-based visual servoing. The experiments show that the framework is robust against moving objects and moderate illumination changes. It is also shown that the system is capable of on-line path learning

Experimental evaluation of an urban visual path following framework

International audienceRobot cars will likely play an important role in the future. In this paper a visual path following framework for urban environments is experimentally evaluated. The framework's hybrid topological-metric approach for representing the environment provides stable interest points for image-based visual servoing during navigation. The presented experimental results with a robot car show that the framework is robust against changing illumination and moving objects covering up parts of the field of view of the monocular camera. Furthermore, there is no need to perform bundle adjustment nor to use odometry

A framework for scalable vision-only navigation

International audienceThis paper presents a monocular vision framework enabling feature-oriented appearance-based navigation in large outdoor environ- ments containing other moving ob jects. The framework is based on a hybrid topological-geometrical environment representation, constructed from a learning sequence acquired during a robot motion under hu- man control. The framework achieves the desired navigation functional- ity without requiring a global geometrical consistency of the underlying environment representation. The main advantages with respect to con- ventional alternatives are unlimited scalability, real-time mapping and effortless dealing with interconnected environments once the loops have been properly detected. The framework has been validated in demanding, cluttered and interconnected environments, under different imaging con- ditions. The experiments have been performed on many long sequences acquired from moving cars, as well as in real-time large-scale navigation trials relying exclusively on a single perspective camera. The obtained results imply that a globally consistent geometric environment model is not mandatory for successful vision-based outdoor navigation

Visual path following using only monocular vision

International audienceThe video shows the French intelligent transportation vehicle CyCab performing visual path following using only monocular vision. All phases of the process are shown with a spoken commentary. In the teaching phase, the user drives the robot manually while images from the camera are stored. Key images with corresponding images features are stored as a map together with 2D and 3D local information. In the navigation phase, CyCab follows the learned path by tracking the images features projected from the map and with a simple visual servoing control law

Extracting Minimalistic Corridor Geometry from Low-Resolution Images

Abstract. We propose a minimalistic corridor representation consisting of the orientation line (center) and the wall-floor boundaries (lateral limit). The representation is extracted from low-resolution images using a novel combination of information theoretic measures and gradient cues. Our study investigates the impact of image resolution upon the accuracy of extracting such a geometry, showing that accurate centerline and wall-floor boundaries can be estimated even in texture-poor environments with images as small as 16 × 12. In a database of 7 unique corridor sequences for orientation measurements, less than 2 % additional error was observed as the resolution of the image decreased by 99%. One of the advantages of working at such resolutions is that the algorithm operates at hundreds of frames per second, or equivalently requires only a small percentage of the CPU. Keywords: Low-Resolution, Robot Navigation, Geometry Estimation