Sensor Fusion and Deep Learning for Indoor Agent Localization

Autonomous, self-navigating agents have been rising in popularity due to a push for a more technologically aided future. From cars to vacuum cleaners, the applications of self-navigating agents are vast and span many different fields and aspects of life. As the demand for these autonomous robotic agents has been increasing, so has the demand for innovative features, robust behavior, and lower cost hardware. One particular area with a constant demand for improvement is localization, or an agent\u27s ability to determine where it is located within its environment. Whether the agent\u27s environment is primarily indoor or outdoor, dense or sparse, static or dynamic, an agent must be able to have knowledge of its location. Many different techniques exist today for localization, each having its strengths and weaknesses. Despite the abundance of different techniques, there is still room for improvement. This research presents a novel indoor localization algorithm that fuses data from multiple sensors for a relatively low cost. Inspired by recent innovations in deep learning and particle filters, a fast, robust, and accurate autonomous localization system has been created. Results demonstrate that the proposed system is both real-time and robust against changing conditions within the environment

3D modeling of indoor environments for a robotic security guard

Autonomous mobile robots will play a major role in future security and surveillance tasks for large scale environments such as shopping malls, airports, hospitals and museums. Robotic security guards will autonomously survey such environments, unless a remote human operator takes over control. In this context a 3D model can convey much more useful information than the typical 2D maps used in many robotic applications today, both for visualisation of information and as human machine interface for remote control. This paper addresses the challenge of building such a model of a large environment (50m x 60m) using data from the robot’s own sensors: a 2D laser scanner and a panoramic camera. The data are processed in a pipeline that comprises automatic, semi-automatic and manual stages. The user can interact with the reconstruction process where necessary to ensure robustness and completeness of the model. A hybrid representation, tailored to the application, has been chosen: floors and walls are represented efficiently by textured planes. Non-planar structures like stairs and tables, which are represented by point clouds, can be added if desired. Our methods to extract these structures include: simultaneous localization and mapping in 2D and wall extraction based on laser scanner range data, building textures from multiple omni-directional images using multi-resolution blending, and calculation of 3D geometry by a graph cut stereo technique. Various renderings illustrate the usability of the model for visualising the security guard’s position and environment

Overcoming barriers and increasing independence: service robots for elderly and disabled people

This paper discusses the potential for service robots to overcome barriers and increase independence of elderly and disabled people. It includes a brief overview of the existing uses of service robots by disabled and elderly people and advances in technology which will make new uses possible and provides suggestions for some of these new applications. The paper also considers the design and other conditions to be met for user acceptance. It also discusses the complementarity of assistive service robots and personal assistance and considers the types of applications and users for which service robots are and are not suitable

An intelligent multi-floor mobile robot transportation system in life science laboratories

In this dissertation, a new intelligent multi-floor transportation system based on mobile robot is presented to connect the distributed laboratories in multi-floor environment. In the system, new indoor mapping and localization are presented, hybrid path planning is proposed, and an automated doors management system is presented. In addition, a hybrid strategy with innovative floor estimation to handle the elevator operations is implemented. Finally the presented system controls the working processes of the related sub-system. The experiments prove the efficiency of the presented system

BatVision: Learning to See 3D Spatial Layout with Two Ears

Many species have evolved advanced non-visual perception while artificial systems fall behind. Radar and ultrasound complement camera-based vision but they are often too costly and complex to set up for very limited information gain. In nature, sound is used effectively by bats, dolphins, whales, and humans for navigation and communication. However, it is unclear how to best harness sound for machine perception. Inspired by bats' echolocation mechanism, we design a low-cost BatVision system that is capable of seeing the 3D spatial layout of space ahead by just listening with two ears. Our system emits short chirps from a speaker and records returning echoes through microphones in an artificial human pinnae pair. During training, we additionally use a stereo camera to capture color images for calculating scene depths. We train a model to predict depth maps and even grayscale images from the sound alone. During testing, our trained BatVision provides surprisingly good predictions of 2D visual scenes from two 1D audio signals. Such a sound to vision system would benefit robot navigation and machine vision, especially in low-light or no-light conditions. Our code and data are publicly available

Mobile robot transportation in laboratory automation

In this dissertation a new mobile robot transportation system is developed for the modern laboratory automation to connect the distributed automated systems and workbenches. In the system, a series of scientific and technical robot indoor issues are presented and solved, including the multiple robot control strategy, the indoor transportation path planning, the hybrid robot indoor localization, the recharging optimization, the robot-automated door interface, the robot blind arm grasping & placing, etc. The experiments show the proposed system and methods are effective and efficient

Navigation of mobile robot in cluttered environment

Now a day’s mobile robots are widely used in many applications. Navigation of mobile robot is primary issue in robotic research field. The mobile robots to be successful, they must quickly and robustly perform useful tasks in a complex, dynamic, known and unknown surrounding. Navigation plays an important role in all mobile robots activities and tasks. Mobile robots are machines, which navigate around their environment extracting sensory information from the surrounding, and performing actions depend on the information given by the sensors. The main aim of navigation of mobile robot is to give shortest and safest path while avoiding obstacles with the help of suitable navigation technique such as Fuzzy logic. In this, we build up mobile robot then simulation and experiments are carried out in the lab. Comparison between the simulation and experimental results are done and are found to be in good