The experimental results show that this approach can provide consistent models of natural environments to facilitate complex visual tracking and data-association problems. Rectilinear forms of snake-like robotic locomotion are anticipated to be an advantage in obstacle-strewn scenarios characterizing urban disaster zones, subterranean collapses, and other natural environments.

Recognising, Representing and Mapping in Field Robotics: A Statistical View to Perception in Unstructured . Multi-level State Estimation in an Outdoor Decentralised Sensor Publications for Fabio Ramos Network. 10th International Symposium on Experimental Robotics (ISER).

Recognising, Representing and Mapping in Field Robotics: A Statistical View to Perception in Unstructured Environments. Germany: VDM Verlag Dr Publications for Fabio Ramos Muller. Ramos, . Brock, . Trinkle, J. (2009). A natural feature representation for unstructured environments. Germany: VDM Verlag Dr Muller. IEEE Transactions on Robotics, 24(6), 1329-1340.

Ramos et al have demonstrated recognition and segmentation of objects in unstructured environments using camera images and generative models. Mixtures are generatively trained through Variational Bayesian Expectation Maximisation (VBEM) for models representing the background and the object. The human brain confronts the same problem in everyday perception, extracting from its high-dimensional sensory inputs-30,000 auditory nerve fibers or 106 optic nerve fibers-a manageably small number of perceptually relevant features.

Machine Perception in Unstructured and Unknown Environments

Machine Perception in Unstructured and Unknown Environments. Steven Scheding, Richard Grover, Hugh Durrant-Whyte. Robot spatial mapping, in this book, is about the problem of a robot computing a representation of its environment from data gathered by its sensors. This problem has been studied since the creation of the first autonomous mobile robot in the late nineteen-sixties.

The experimental results show that this approach can provide consistent models of natural environments to facilitate complex visual tracking and data-association problems. cle{Ramos2008ANF, title {A Natural Feature Representation for Unstructured Environments}, author {Fabio Tozeto Ramos and Suresh Kumar and Ben Upcroft and Hugh F. Durrant-Whyte}, journal {IEEE Transactions on Robotics}, year {2008}, volume {24}, pages {1329-1340} }.

Hilbert maps: scalable continuous occupancy mapping with stochastic . Recognising and modelling landmarks to close loops in outdoor slam. FT Ramos, J Nieto, HF Durrant-Whyte.

Hilbert maps: scalable continuous occupancy mapping with stochastic gradient descent. 2014 IEEE International Conference on Robotics and Automation (ICRA), 6136-6143, 2014. Proceedings 2007 IEEE International Conference on Robotics and Automatio. 2007.

Walking in Unstructured Natural Environments. Navigation and Mapping in Large Unstructured Environments. This allows us to determine color and texture information of the 3D points in the field of view of each camera

Walking in Unstructured Natural Environments. legged robot to walk in unstructured environ- ments. executed every time a specific sensorial pat-. Terrain Appreciation in Virtual Environments: Spatial. This allows us to determine color and texture information of the 3D points in the field of view of each camera. In unstructured environments, classification of the terrain can be challenging due to sensor noise, varying density of the data, egomotion or percussions on rough terrain.

Robotic perception, in the scope of this chapter, encompasses the ML algorithms and techniques that empower robots to learn from sensory data and, based on learned models, to react and take decisions accordingly. The recent developments in machine learning, namely deep-learning approaches, are evident and, consequently, robotic perception systems are evolving in a way that new applications and tasks are becoming a reality

Fabio Ramos3 The University of Sydney. Learning a model of an environment that is correctly able to distinguish occupied and unoccupied areas is important for maneuvering robots in unstructured envi-ronments.

Fabio Ramos3 The University of Sydney. A common technique to tackle such problems is to train a classier with hand-crafted features that encode occupancy information. However, nding good features quickly becomes computationally prohibitive and impractical for complex and large environments.