Despite much research on patch-based descriptors, SIFT remains the gold standard for finding correspondences across images and recent descriptors focus primarily on improving speed rather than accuracy. In this paper we propose Descriptor-Nets (D-Nets), a computationally efficient method that significantly improves the accuracy of image matching by going beyond patch-based approaches. D-Nets constructs a network in which nodes correspond to traditional sparsely or densely sampled keypoints, and where image content is sampled from selected edges in this net. Not only is our proposed representation invariant to cropping, translation, scale, reflection and rotation, but it is also significantly more robust to severe perspective and non-linear distortions. We present several variants of our algorithm, including one that tunes itself to the image complexity and an efficient parallelized variant that employs a fixed grid. Comprehensive direct comparisons against SIFT and ORB on standard datasets demonstrate that D-Nets dominates existing approaches in terms of precision and recall while retaining computational efficiency.

Tracking of extended targets in high definition 360 degree 3D-LIDAR (Light Detection and Ranging) measurements is a challenging task. It is a key component in robotic applications and is relevant to collision avoidance and autonomous driving. This paper presents a robust method to determine the 3D outline contour of vehicles in disordered 3D-LIDAR measurements while using several geometrical vehicle-specific constraints. In addition, the 3D outline contour contains information on the local reliability of the contour. A weighted registration approach allows calculating the velocity of consecutive 3D outline contours directly. The approach is tested with real sensor data. A robot car equipped with an inertial measurement unit serves as ground truth.

In this paper we describe a novel approach to model-based monocular vehicle tracking out of a moving vehicle using active vision. The designed algorithm can cope with cluttered color images, complex lighting conditions as well as partial occlusion of the leading vehicle and is able to detect and track a vehicle even within unstructured offroad environments. Thanks to the used 3D model which describes the characteristic vehicle geometry and appearance in terms of vertexes, edges and colored surfaces, no special visual markers are required. The knowledge of vehicle's geometry and appearance gained from the model are used within a particle filter to estimate the 6DoF position relative to the ego vehicle, thereby fusing edge as well as color information. We successfully use the proposed algorithm for pure vision based autonomous offroad convoy driving.

An automatic parking system relies on precise estimation of parking space geometry. This paper proposes the use of a hierarchical three dimensional occupancy grid for the detection of parking spaces. The occupancy grid covers the environment representation of the static world. A hierarchical design allows dynamic selection of the level of detail. Applying a three-dimensional grid provides the additional benefit of supporting a variety of other functions including height estimation using a single environment representation type. The presented approach derives the distance to obstacles and walls and thus is able to represent the free space that forms parking spaces. In a second step, the dimensions of the parking space are calculated. For evaluation, real parking spaces are detected and estimated using short range radar sensors. The calculated dimensions are compared to the ground truth.

This paper presents a novel and robust method to determine the outline contour of vehicles in 3D-LIDAR (Light Detection and Ranging) measurements. To calculate the outline contour, a vehicle is described by its geometrical properties. These properties are used as constraints to fit a surface to unordered, scattered and error-contaminated 3D measurements. The surface can be used to calculate a corresponding 2D outline contour. The algorithm is tested with two different laser scanners. One scanner has 64, the other only 4 layers.

In this paper we describe a novel approach to autonomous dirt road following. The algorithm is able to recognize highly curved roads in cluttered color images quite often appearing in offroad scenarios. To cope with large curvatures we apply gaze control and model the road using two different clothoid segments. A Particle Filter incorporating edge and color intensity information is used to simultaneously detect and track the road farther away from the ego vehicle. In addition the particles are used to generate static road segment estimations in a given look ahead distance. These estimations are predicted with respect to ego motion and fused utilizing Kalman filter techniques to generate a smooth local clothoid segment for lateral control of the vehicle.

This paper describes a fast method for segmentation of large-size long-range 3D point clouds that especially lends itself for later classification of objects. Our approach is targeted at high-speed autonomous ground robot mobility, so real-time performance of the segmentation method plays a critical role. This is especially true as segmentation is considered only a necessary preliminary for the more important task of object classification that is itself computationally very demanding. Efficiency is achieved in our approach by splitting the segmentation problem into two simpler subproblems of lower complexity: local ground plane estimation followed by fast 2D connected components labeling. The method's performance is evaluated on real data acquired in different outdoor scenes, and the results are compared to those of existing methods. We show that our method requires less runtime while at the same time yielding segmentation results that are better suited for later classification of the identified objects.

The essence of the signal-to-symbol problem consists of associating a symbolic description of an object (e.g., a chair) to a signal (e.g., an image) that captures the real object. Robots that interact with humans in natural environments must be able to solve this problem correctly and robustly. However, the problem of providing complete object models a priori to a robot so that it can understand its environment from any viewpoint is extremely difficult to solve. Additionally, many objects have different uses which in turn can cause ambiguities when a robot attempts to reason about the activities of a human and their interactions with those objects. In this paper, we build upon the fact that robots that co-exist with humans should have the ability of observing humans using the different objects and learn the corresponding object definitions. We contribute an object recognition algorithm, FOCUS, that is robust to the variations of signals, combines structure and function of an object, and generalizes to multiple similar objects. FOCUS, which stands for Finding Object Classification through Use and Structure, combines an activity recognizer capable of capturing how an object is used with a traditional visual structure processor. FOCUS learns structural properties (visual features) of objects by knowing first the object's affordance properties and observing humans interacting with that object with known activities. The strength of the method relies on the fact that we can define multiple aspects of an object model, i.e., structure and use, that are individually robust but insufficient to define the object, but can do when combined.