Building and Navigating Maps Of Road Scenes Using An Active Sensor

The author presents algorithms for building maps of road scenes using an active range and reflectance sensor and for using the maps to traverse a portion of the world already explored. He describes some advantages of an active sensor, namely, that it is independent of the illumination conditions, does not require complex calibration in order to transform observed features to the vehicle's reflectance frame, and provides 3-D terrain models as well as road models. Using this map built from sensor data facilitates navigation in two respects: the vehicle may navigate faster, since less perception processing is necessary, and the vehicle may follow a more accurate path, since the navigation system does not rely entirely on inaccurate visual data. The author presents a complete system that includes road-following, map-building, and map-based navigation using the ERIM laser rangefinder. Experimental results are presente

Active and Passive Range Sensing for Robotics

In this paper, we present a brief survey of the technologies currently available for range sensing, of their use in robotics applications, and of emerging technologies for future systems. The paper is organized by type of sensing: laser range finders, triangulation range finders, and passive stereo. A separate section focuses on current development in the area of nonscanning sensors, a critical area to achieve range sensing performance comparable to that of conventional cameras. The presentation of the different technologies is based on many recent examples from robotics research

Omni-Directional Structure From Motion

In this paper, we describe our work on a robot navigation system using an omnidirectional camera as the primary sensor. An omnidirectional structure from motion algorithm is presented with its uncertainty analyzed. We evaluate the omnidirectional SFM on both synthetic data and on real image sequences. Comparison with the conventional camera is made and we show that in certain situations the omnidirectional SFM gives better results than the conventional one by taking advantage of its larger field of view

Incorporating Background Invariance into Feature-Based Object Recognition

Current feature-based object recognition methods use information derived from local image patches. For robustness, features are engineered for invariance to various transformations, such as rotation, scaling, or affine warping. When patches overlap object boundaries, however, errors in both detection and matching will almost certainly occur due to inclusion of unwanted background pixels. This is common in real images, which often contain significant background clutter, objects which are not heavily textured, or objects which occupy a relatively small portion of the image. We suggest improvements to the popular Scale Invariant Feature Transform (SIFT) which incorporate local object boundary information. The resulting feature detection and descriptor creation processes are invariant to changes in background.We call this method the Background and Scale Invariant Feature Transform (BSIFT).We demonstrate BSIFT’s superior performance in feature detection and matching on synthetic and natural imag

A Hierarchical Field Framework for Unified Context-Based Classification

We present a two-layer hierarchical formulation to exploit different levels of contextual information in images for robust classification. Each layer is modeled as a conditional field that allows one to capture arbitrary observationdependent label interactions. The proposed framework has two main advantages. First, it encodes both the short-range interactions (e.g., pixelwise label smoothing) as well as the long-range interactions (e.g., relative configurations of objects or regions) in a tractable manner. Second, the formulation is general enough to be applied to different domains ranging from pixelwise image labeling to contextual object detection. The parameters of the model are learned using a sequential maximum-likelihood approximation. The benefits of the proposed framework are demonstrated on four different datasets and comparison results are presented

Pairwise Grouping Using Color

Grouping was recognized in computer vision early on as having the potential of improving both matching and recognition. Most papers consider grouping as a segmentation problem and a hard decision is made about which pixels in the image belong to the same object. In this paper we instead focus on soft pairwise grouping, that is computing affinities between pairs of pixels that reflect how likely that pair is to belong to the same object. This fits perfectly with our recognition approach, where we consider pairwise relationships between features/pixels. Some other papers also considered soft pairwise grouping between features, but they focused more on geometry than appearance. In this paper we take a different approach and show how color could also be used for pairwise grouping. We present a simple but effective method to group pixels based on color statistics. By using only color information and no prior higher level knowledge about objects and scenes we develop an efficient classifier that can separate the pixels that belong to the same object from those that do not. In the context of segmentation where color is also used only nearby pixels are generally considered, and very simple color information is taken into account. We use global color information instead and develop an efficient algorithm that can successfully classify even pairs of pixels that are far apar

Multi-Scale Classification of 3D Objects

We describe an approach to the classification of 3-D objects using a multi-scale representation. This approach starts with a smoothing algorithm for representing objects at different scales. In a way similar to the classical scale space representations, larger amount of smoothing removes more details from the surfaces. Smoothing is applied in curvature space directly, thus avoiding the usual shrinkage problems and allowing for efficient implementations. A 3-D similarity measure that integrates the representations of the objects at multiple scales is introduced. This similarity measure is designed to give higher weight to the coarse scale representations, while ignoring the finer scale details of the surfaces. Given a library of models, objects that are similar based on this multi-scale measure are grouped together into classes. We show how shapes in a given class can be combined into a single prototype object. This is achieved by using a powerful property, introduced earlier, of inverse mapping from representation to shape. Finally, the prototypes are used for hierarchical recognition by first comparing the scene representation to the prototypes and then matching it only to the objects in the most likely class rather than to the entire library of models. Beyond its application to object recognition, this approach provides an attractive implementation of the intuitive notions of scale and approximate similarity for 3-D shapes

Experimental Comparison of Techniques for Localization and Mapping Using A Bearing-Only Sensor

We present a comparison of an extended Kalman filter and an adaptation of bundle adjustment from computer vision for mobile robot localization and mapping using a bearing-only sensor. We show results on synthetic and real examples and discuss some advantages and disadvantages of the techniques. The comparison leads to a novel combination of the two techniques which results in computational complexity near Kalman filters and performance near bundle adjustment on the examples shown

Evaluation of Image-Based Landmark Recognition Techniques

Recognizing landmarks in sequences of images is a challenging problem for a number of reasons. First of all, the appearance of any given landmark varies substantially from one observation to the next. In addition to variations due to different aspects, an illumination change, external clutter, and changing geometry of the imaging devices are other factors affecting the variability of the observed landmarks. Finally, it is typically difficult to make use of accurate 3D information in landmark recognition applications. For those reasons, it is not possible to use many of the object recognition techniques based on strong geometric models. The alternative is to use image-based techniques in which landmarks are represented by collections of images which capture the “typical” appearance of the object. The information most relevant to recognition is extracted from the collection of raw images and used as the model for recognition. This process is often referred to as “visual learning.” Models of landmarks are acquired from image sequences and later recognized for vehicle localization in urban environments. In the acquisition phase, a vehicle drives and collects images of an unknown area. The algorithm organizes these images into groups with similar image features. The feature distribution for each group describes a landmark. In the recognition phase, while navigating through the same general area, the vehicle collects new images. The algorithm classifies these images into one of the learned groups, thus recognizing a landmark. Unlike computationally intensive model-based approaches that build models from known objects observed in isolation, our image-based approach automatically learns the most salient landmarks in complex environments. It delivers a robust performance under a wide range of lighting and imaging angle variations

Where and When to Look: How to Extend the Myopic Planning Horizon

In this paper we describe an approach towards integrating mid-range sensing data into a dynamic path planning algorithm. The key problem, sensing for planning is addressed in the context of outdoor navigation. An algorithmic approach is described towards solving these problems and both simulation results and initial experimental results for outdoor navigation using wide baseline stereo data are presented