The Cloud Slam conference is the world's premier cloud computing event, covering technology, business models, industry experiences, legal aspects, research, development and innovations in the world of cloud computing. This podcast contains audio tracks of all conference sessions and panels, in MP3 format.
This IoT Grand Slam conference features expert contributions from elite IoT practitioners at leading-edge organizations that are corporate members of the IoT Community including HPE, ClearBlade Inc, SAS, Oracle, Valuer, Intertrust, Inmarsat, Zebra Technologies, CBT, Cisco, Syandia, Phoenix Contact, Link Labs, Red Hat, among other leading industry players, and IoT Practitioners from the IoT ecosystem.
Cloud Slam ‘09 Conference
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They will be discussing and demonstrating business applications and thought leadership around the deployment of IoT in the corporate, enterprise, and industrial ecosystems at this marquee IoT conference.
SLAM will always use several different types of sensors, and the powers and limits of various sensor types have been a major driver of new algorithms.[7] Statistical independence is the mandatory requirement to cope with metric bias and with noise in measurements. Different types of sensors give rise to different SLAM algorithms which assumptions are most appropriate to the sensors. At one extreme, laser scans or visual features provide details of many points within an area, sometimes rendering SLAM inference unnecessary because shapes in these point clouds can be easily and unambiguously aligned at each step via image registration. At the opposite extreme, tactile sensors are extremely sparse as they contain only information about points very close to the agent, so they require strong prior models to compensate in purely tactile SLAM. Most practical SLAM tasks fall somewhere between these visual and tactile extremes.
The automatic navigation of the intelligent wheelchair is a major challenge for its applications. Most previous researches mainly focus on 2D navigation of intelligent wheelchair, which loses many useful environment information. This paper proposed a novel Grid-Point Cloud-Semantic Multi-layered Map based on graph optimization for intelligent wheelchair navigation. For mapping, the 2D grid map is at the bottom, the 3D point cloud map is on the grid map and the semantic markers are labelled in them. The semantic markers combine the name and coordinate value of object marker together. For navigation, the wheelchair uses the grid map for localization and path planning, uses the point cloud map for feature extraction and obstacle avoidance, and uses the semantic markers for human-robot vocal interaction. A number of experiments are carried out in real environments to verify its feasibility.
In order to cope with various situations of noise distribution, cluster-based noise reduction methods have been paid more and more attention. Clustering is an analytical method that uses distance or similarity between data to divide an entire dataset into groups. DBSCAN method is widely used in noise reduction [6]. It can cluster various shapes and effectively remove the clustered and isolated outliers. J. Kim et al. [8] proposed a graph-based spatial clustering algorithm. The algorithm uses the Delaunay triangulation tool commonly used in geometric spatial analysis and the concept of adjacent objects in DBSCAN to better segment point cloud while reducing clustering background noise. However, these methods are accompanied by high time complexity and often need to be accelerated. Running them in real-time is difficult for indoor mobile robots with limited computing resources.
We use the angle difference threshold (θthreshold) and Euclidean distance threshold (dthreshold) to judge whether the two laser-point cloud blocks can be merged. The Euclidean distance threshold is judged only if the angle difference threshold is met. If the angle threshold is not met, it is directly considered that the two laser-point cloud blocks cannot be merged. We only calculate the two points with the closest angles of the two laser-point cloud blocks to judge whether the above thresholds can be met. Because the scan data are in the polar coordinate system, the Euclidean distance is calculated as in Formula (9):
Laser-point cloud blocks cannot be merged. Calculate using points i and i+1, d is the distance between the two points. (a) The angle threshold is met, but the distance threshold is not. (b) The angle threshold is not met.
A frame of LiDAR scanning in the simulation environment. The points in the figure represent the point cloud of obstacles. (i) Indicates an abnormal laser point; (ii) refers to a robot. The side length of each grid is 1 m.
We are very excited to be holding the ICLR 2023 annual conference in Kigali, Rwanda this year from May 1-5, 2023. The conference will be located at the beautiful Kigali Convention Centre / Radisson Blu Hotel location which was recently built and opened for events and visitors in 2016. The Kigali Convention Centre is located 5 kilometers from the Kigali International Airport.
Appeared in 30 games this season and started 26... she pitched 155.2 innigs (9th in the G-MAC) and had 13 complete games (8th in the G-MAC) ... won 18 games which was the second most in the G-MAC ... her 1.98 ERA was third best in the conference ... she earned five shutouts which was fifth most in the conference ... her 128 strikeouts and 26 strikeouts looking were good for sixth best in the G-MAC ... she allowed an opponents batting average of .242 which was seventh best in the conference. 2ff7e9595c