Robot navigation

Robot localization denotes the robot's ability to establish its own position and orientation within the frame of reference. Path planning is effectively an extension of localisation, in that it requires the determination of the robot's current position and a position of a goal location, both within the same frame of reference or coordinates. Map building can be in the shape of a metric map or any notation describing locations in the robot frame of reference. For any mobile device, the ability to navigate in its environment is important. Avoiding dangerous situations such as collisions and unsafe conditions (temperature, radiation, exposure to weather, etc.) comes first, but if the robot has a purpose that relates to specific places in the robot environment, it must find those places. This article will present an overview of the skill of navigation and try to identify the basic blocks of a robot navigation system, types of navigation systems, and closer look at its related building co

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Real-Time Wide-Baseline Place Recognition Using Depth Completion

Zetao Chen

Place recognition is an essential capability for robotic autonomy. While ground robots observe the world from generally similar viewpoints over repeated visits, other robots, such as small aircraft, experience far more different viewpoints, requiring place recognition for images captured from very wide baselines. While traditional feature-based methods fail dramatically under extreme viewpoint changes, deep learning approaches demand heavy runtime processing. Driven by the need for cheaper alternatives able to run on computationally restricted platforms, such as small aircraft, this work proposes a novel real-time pipeline employing depth-completion on sparse feature maps that are anyway computed during robot localization and mapping, to enable place recognition at extreme viewpoint changes. The proposed approach demonstrates unprecedented precision-recall rates on challenging benchmarking and own synthetic and real datasets with up to 45◦ difference in viewpoints. In particular, our synthetic datasets are, to the best of our knowledge, the first to isolate the challenge of viewpoint changes for place recognition, addressing a crucial gap in the literature. All of the new datasets are publicly available to aid benchmarking.

2019

Coevolution of active vision and feature selection

Dario Floreano, Toshifumi Kato, Davide Marocco, Eric Sauser

We show that complex visual tasks, such as position- and size-invariant shape recognition and navigation in the environment, can be tackled with simple architectures generated by a coevolutionary process of active vision and feature selection. Behavioral machines equipped with primitive vision systems and direct pathways between visual and motor neurons are evolved while they freely interact with their environments. We describe the application of this methodology in three sets of experiments, namely, shape discrimination, car driving, and robot navigation. We show that these systems develop sensitivity to a number of oriented, retinotopic, visual-feature-oriented edges, corners, height, and a behavioral repertoire to locate, bring, and keep these features in sensitive regions of the vision system, resembling strategies observed in simple insects.

2004

Automatic Calibration of Ultra Wide Band Tracking Systems Using A Mobile Robot: A Person Localization Case-study

Alessio Canepa, Alcherio Martinoli, Zeynab Talebpour

Ultra Wide Band (UWB) is an emerging technology in the field of indoor localization, mainly due to its high performances in indoor scenarios and relatively easy deployment. However, in complex indoor environments, its positioning accuracy may drastically decrease due to biases introduced when emitters and receivers operate in Non Line-of-Sight (NLOS) conditions. This undesired phenomenon can be attenuated by creating, a priori, a map of the measurement error in the environment, that can be exploited at a later stage by a localization algorithm. In this paper, the error map is the result of a calibration process, which consists of collecting several measurements of the localization system at different locations in the environment. This work proposes the leveraging of mobile robots in order to automatize the calibration process with the ultimate purpose of improving UWB-based people localization in a realistic indoor environment. The whole process exploits existing algorithms in the field of robot localization conveniently adapted in order to address our use case and technology. Experiments in real environments of incrementally increasing complexity show how the average localization accuracy can be improved up to 50% by adopting this method.

Ieee2017

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