4,818 research outputs found
Probabilistic Surfel Fusion for Dense LiDAR Mapping
With the recent development of high-end LiDARs, more and more systems are
able to continuously map the environment while moving and producing spatially
redundant information. However, none of the previous approaches were able to
effectively exploit this redundancy in a dense LiDAR mapping problem. In this
paper, we present a new approach for dense LiDAR mapping using probabilistic
surfel fusion. The proposed system is capable of reconstructing a high-quality
dense surface element (surfel) map from spatially redundant multiple views.
This is achieved by a proposed probabilistic surfel fusion along with a
geometry considered data association. The proposed surfel data association
method considers surface resolution as well as high measurement uncertainty
along its beam direction which enables the mapping system to be able to control
surface resolution without introducing spatial digitization. The proposed
fusion method successfully suppresses the map noise level by considering
measurement noise caused by laser beam incident angle and depth distance in a
Bayesian filtering framework. Experimental results with simulated and real data
for the dense surfel mapping prove the ability of the proposed method to
accurately find the canonical form of the environment without further
post-processing.Comment: Accepted in Multiview Relationships in 3D Data 2017 (IEEE
International Conference on Computer Vision Workshops
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
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