This paper presents a strategy for achieving practical mapping navigation using a wheeled mobile robot equipped with an advanced sonar sensor The original mapping navigation experiment, carried out with the same robot configuration, built a feature map consisting of common-place indoor landmarks crucial for localization; namely, planes, corners, and edges. The map exhaustively maintained covariance matrices among all features, and thus presented a time and memory impediment to practical navigation in large environments. The new local mapping strategy proposed here breaks down a large environment into a topology of local regions, only maintaining the covariance among features in the same local region, and the covariance among local maps. This notion of two-hierarchy representation drastically improves the memory and processing-time requirements of the original global approach, while preserving the statistical details, in the authors' opinions, necessary for an accurate map and prolonged navigation. The new...

Feature-Based Mapping in Real, Large Scale Environments Using an Ultrasonic Array:

In many applications, mobile robots must be able to localize themselves with respect to environments which are not known a priori in order to navigate and accomplish tasks. This means that the robot must be able to build a map of an unknown environment while simultaneously localizing itself within that map. The so called Simultaneous Localization and Mapping or SLAM problem is a formulation of this requirement, and has been the subject of a considerable amount of robotics research in the last decade. 
This thesis looks at the problem of localization and mapping when the only information available to the robot is measurements of relative motion and bearings to features. The relative motion sensor measures displacement from one time to the next through some means such as inertial measurement or odometry, as opposed to externally referenced position measurements like compass or GPS. The bearing sensor measures the direction toward features from the robot through a sensor such as an omnidirectional camera, as opposed to bearing and range sensors such as laser rangefinders, sonar, or millimeter wave radar. 
A full solution to the bearing-only SLAM problem must take into consideration detecting and identifying features and estimating the location of the features as well as the motion of the robot using the measurements. This thesis focuses on the estimation problem given that feature detection and data association are available. Estimation requires a solution that is fast, accurate, consistent, and robust. 
In an applied sense, this dissertation puts forth a methodology for building maps and localizing a mobile robot using odometry and monocular vision. This sensor suite is chosen for its simplicity and generality, and in some sense represents a minimal configuration for localization and mapping. 
In a broader sense, the dissertation describes a novel method for state estimation applicable to problems which exhibit particular nonlinearity and sparseness properties. The method relies on deterministic sampling in order to compute sufficient statistics at each time step in a recursive filter. The relationship of the new algorithm to bundle adjustment and Kalman filtering (including some of its variants) is discussed.

Bearings-only localization and mapping

Biologically Plausible Spatial Navigation for a Mobile Robot

This paper presents how robust 1-pixel-wide and 8-connected skeletons can be obtained simultaneously with exact distance transform calculation. The proposed approach is based on the new concept of exact dilation. Two alternative algorithms for exact Euclidean distance transform calculation allowing exact dilation are described: a simpler approach based on the SEDR (sorted exact distance representation) data structure, which allows exact distance transform calculation; and a more effective strategy based on border propagation. In both techniques the distances are assigned strictly according to sequences of increasing exact distances in the orthogonal lattice. Because of the high accuracy allowed by such procedures, progressive dilations, high quality and accurate 1-pixel-wide and 8-connected skeletons can be obtained corresponding to the frontiers between previously labelled distinct connected objects. Although this method can be useful for determining generalized Dirichlet tessellations, which is also illustrated in this article, its full potential is harnessed by previously segmenting the contours of connected objects by removing their points corresponding to curvature peaks, which are obtained by using an effective multi-scale curvature estimation technique. In such a way, not only high-quality 1-pixel-wide and 8-connected skeletons are obtained for any shape, but also the whole approach becomes considerably robust to small distortions in the object contours, thus avoiding one of the great shortcomings in traditional skeletonization methods. The application of such methods to an important problem in computational neuroscience and neuromorphometry, namely the automated extraction of tapered dendrograms, is described and illustrated. Considerations regarding distances in orthogonal lattices, typical problems in skeletonization, and the practical implementation of the proposed techniques are also included.

Robust Skeletonization through Exact Euclidean Distance Transform and its Application to Neuromorphometry

This work explores techniques for sonar sensor fusion in the context of environmental feature detection and identification for navigation tasks. By detecting common features in indoor environments and using them as landmarks, a robot can navigate reliably, recovering its pose when necessary. Preliminary results on a multiple hypothesis testing procedure for feature localization and identification show that accurate feature information can be acquired with adequate sonar models and configurations. In addition, a method that associates sonar configuration with the precision of feature extraction is discussed, as well as its utility for guiding an active sonar sensor.

/pdf/feature-detection-and-identification-using-a-sonar-array-4acnf4imfi.pdf

Feature detection and identification using a sonar-array

This paper presents an environmental acquisition strategy for a mobile robot using an advanced sonar sensor to achieve mapping navigation in an a priori unknown, imperfectly structured indoor environment. Most existing feature based strategies rely on unrealistic assumptions about the environment, while their grid based counterparts hinder localisation which leads to rapid degradation of map quality. A dual representation strategy is proposed here which exploits the strength of both a feature map and a grid map. With the advantage sensor, the environment is scanned and the obtained features are classified into planes, corners, edges and unknowns. The feature map is only updated with the first three types of features. The grid map is updated with all measurement, including the unknowns resulting from complicated objects, to enable obstacle avoidance. On the grid map, distance transform based exploratory path planning is implemented. Adaptation has been made so that an explore-local-first behaviour is exhibited.

K.S. Chong

Papers

Indoor exploration using a sonar sensor array: a dual representation strategy