6D SLAM with Cached kd-tree Search

6D SLAM (Simultaneous Localization and Mapping) or 6D Concurrent Localization and Mapping of mobile robots considers six degrees of freedom for the robot pose, namely, the x, y and z coordinates and the roll, yaw and pitch angles. In previous work we presented our scan matching based 6D SLAM approach, where scan matching is based on the well known iterative closest point (ICP) algorithm [Besl 1992]. Efficient implementations of this algorithm are a result of a fast computation of closest points. The usual approach, i.e., using kd-trees is extended in this paper. We describe a novel search stategy, that leads to significant speed-ups. Our mapping system is real-time capable, i.e., 3D maps are computed using the resources of the used Kurt3D robotic hardware

Turning an action formalism into a planner

The paper describes a case study that explores the idea of building a planner with a neat semantics of the plans it produces, by choosing some action formalism that is "ideal" for the planning application and building the planner accordingly. In general-and particularly so for the action formalism used in this study, which is quite expressive-this strategy is unlikely to yield fast and efficient planners if the formalism is used naively. Therefore, we adopt the idea that the planner approximates the theoretically ideal plans, where the approximation gets closer, the more run time the planner is allowed. As the particular formalism underlying our study allows a significant degree of uncertainty to be modeled and copes with the ramification problem, we end up in a planner that is functionally comparable to modern anytime uncertainty planners, yet is based on a neat formal semantics. To appear in the Journal of Logic and Computation, 1994. The paper is written in English

Rmagine: 3D Range Sensor Simulation in Polygonal Maps via Raytracing for Embedded Hardware on Mobile Robots

Sensor simulation has emerged as a promising and powerful technique to find solutions to many real-world robotic tasks like localization and pose tracking.However, commonly used simulators have high hardware requirements and are therefore used mostly on high-end computers. In this paper, we present an approach to simulate range sensors directly on embedded hardware of mobile robots that use triangle meshes as environment map. This library called Rmagine allows a robot to simulate sensor data for arbitrary range sensors directly on board via raytracing. Since robots typically only have limited computational resources, the Rmagine aims at being flexible and lightweight, while scaling well even to large environment maps. It runs on several platforms like Laptops or embedded computing boards like Nvidia Jetson by putting an unified API over the specific proprietary libraries provided by the hardware manufacturers. This work is designed to support the future development of robotic applications depending on simulation of range data that could previously not be computed in reasonable time on mobile systems

Online Context-based Object Recognition for Mobile Robots

This work proposes a robotic object recognition system that takes advantage of the contextual information latent in human-like environments in an online fashion. To fully leverage context, it is needed perceptual information from (at least) a portion of the scene containing the objects of interest, which could not be entirely covered by just an one-shot sensor observation. Information from a larger portion of the scenario could still be considered by progressively registering observations, but this approach experiences difficulties under some circumstances, e.g. limited and heavily demanded computational resources, dynamic environments, etc. Instead of this, the proposed recognition system relies on an anchoring process for the fast registration and propagation of objects’ features and locations beyond the current sensor frustum. In this way, the system builds a graphbased world model containing the objects in the scenario (both in the current and previously perceived shots), which is exploited by a Probabilistic Graphical Model (PGM) in order to leverage contextual information during recognition. We also propose a novel way to include the outcome of local object recognition methods in the PGM, which results in a decrease in the usually high CRF learning complexity. A demonstration of our proposal has been conducted employing a dataset captured by a mobile robot from restaurant-like settings, showing promising results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

06231 Abstracts Collection -- Towards Affordance-Based Robot Control

From June 5 to June 9, 2006, the Dagstuhl Seminar 06231 ``Towards Affordance-Based Robot Control\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. %The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available. Additionally, papers related to a selection of the above-mentioned presentations willbe published in a proceedings volume (Springer LNAI) early in 2007

MICP-L: Mesh-based ICP for Robot Localization using Hardware-Accelerated Ray Casting

Triangle mesh maps have proven to be a versatile 3D environment representation for robots to navigate in challenging indoor and outdoor environments exhibiting tunnels, hills and varying slopes. To make use of these mesh maps, methods are needed that allow robots to accurately localize themselves to perform typical tasks like path planning and navigation. We present Mesh ICP Localization (MICP-L), a novel and computationally efficient method for registering one or more range sensors to a triangle mesh map to continuously localize a robot in 6D, even in GPS-denied environments. We accelerate the computation of ray casting correspondences (RCC) between range sensors and mesh maps by supporting different parallel computing devices like multicore CPUs, GPUs and the latest NVIDIA RTX hardware. By additionally transforming the covariance computation into a reduction operation, we can optimize the initial guessed poses in parallel on CPUs or GPUs, making our implementation applicable in real-time on a variety of target architectures. We demonstrate the robustness of our localization approach with datasets from agriculture, drones, and automotive domains

Salient Visual Features to Help Close the Loop in 6D SLAM

One fundamental problem in mobile robotics research is _Simultaneous Localization and Mapping_ (SLAM): A mobile robot has to localize itself in an unknown environment, and at the same time generate a map of the surrounding area. One fundamental part of SLAM algorithms is loop closing: The robot detects whether it has reached an area that has been visited before, and uses this information to improve the pose estimate in the next step. In this work, visual camera features are used to assist closing the loop in an existing 6 degree of freedom SLAM (6D SLAM) architecture. For our robotics application we propose and evaluate several detection methods, including salient region detection and maximally stable extremal region detection. The detected regions are encoded using SIFT descriptors and stored in a database. Loops are detected by matching of the images' descriptors. A comparison of the different feature detection methods shows that the combination of salient and maximally stable extremal regions suggested by Newman and Ho performs moderately