Efficient Approach for Extracting High-Level B-Spline Features from LIDAR Data for Light-Weight Mapping

Light-weight and accurate mapping is made possible by high-level feature extraction from sensor readings. In this paper, the high-level B-spline features from a 2D LIDAR are extracted with a faster method as a solution to the mapping problem, making it possible for the robot to interact with its environment while navigating. The computation time of feature extraction is very crucial when mobile robots perform real-time tasks. In addition to the existing assessment measures of B-spline feature extraction methods, the paper also includes a new benchmark time metric for evaluating how well the extracted features perform. For point-to-point association, the most reliable vertex control points of the spline features generated from the hints of low-level point feature FALKO were chosen. The standard three indoor and one outdoor data sets were used for the experiment. The experimental results based on benchmark performance metrics, specifically computation time, show that the presented approach achieves better results than the state-of-the-art methods for extracting B-spline features. The classification of the methods implemented in the B-spline features detection and the algorithms are also presented in the paper.

The random exploration graph for optimal exploration of unknown environments

The exploration of an unknown environment by a robot system is a well-studied problem in robotics; however, although many of the proposals made in this field represent efficient tools in terms of exploration paradigm, most of them are not efficient for time critical applications since the robot may visit the same place more than once during backtracking. In this way and considering these limitations, this article presents a novel approach called the random exploration graph, which addresses the problem of exploring unknown environments by building a graph structure created incrementally by the random choice of the free frontier in the observation range of the robot. In addition, the random exploration graph algorithm uses a new concept called “frontier control” introduced in this work, used to store nodes left behind in the graph structure that have not been fully explored and that will be used to guide the exploration process in an efficient way, when the algorithm needs to go back to previously visited areas to continue exploration. The frontier control concept next to the versatility of the graph structure used for the exploration process is the main contribution of this work.

A general purpose feature extractor for light detection and ranging data

Feature extraction is a central step of processing Light Detection and Ranging (LIDAR) data. Existing detectors tend to exploit characteristics of specific environments: corners and lines from indoor (rectilinear) environments, and trees from outdoor environments. While these detectors work well in their intended environments, their performance in different environments can be poor. We describe a general purpose feature detector for both 2D and 3D LIDAR data that is applicable to virtually any environment. Our method adapts classic feature detection methods from the image processing literature, specifically the multi-scale Kanade-Tomasi corner detector. The resulting method is capable of identifying highly stable and repeatable features at a variety of spatial scales without knowledge of environment, and produces principled uncertainty estimates and corner descriptors at same time. We present results on both software simulation and standard datasets, including the 2D Victoria Park and Intel Research Center datasets, and the 3D MIT DARPA Urban Challenge dataset.