Robust sound source mapping using three-layered selective audio rays for mobile robots

© 2016 IEEE. This paper investigates sound source mapping in a real environment using a mobile robot. Our approach is based on audio ray tracing which integrates occupancy grids and sound source localization using a laser range finder and a microphone array. Previous audio ray tracing approaches rely on all observed rays and grids. As such observation errors caused by sound reflection, sound occlusion, wall occlusion, sounds at misdetected grids, etc. can significantly degrade the ability to locate sound sources in a map. A three-layered selective audio ray tracing mechanism is proposed in this work. The first layer conducts frame-based unreliable ray rejection (sensory rejection) considering sound reflection and wall occlusion. The second layer introduces triangulation and audio tracing to detect falsely detected sound sources, rejecting audio rays associated to these misdetected sounds sources (short-term rejection). A third layer is tasked with rejecting rays using the whole history (long-term rejection) to disambiguate sound occlusion. Experimental results under various situations are presented, which proves the effectiveness of our method

GUARDIANS final report

Emergencies in industrial warehouses are a major concern for firefghters. The large dimensions together with the development of dense smoke that drastically reduces visibility, represent major challenges. The Guardians robot swarm is designed to assist fire fighters in searching a large warehouse. In this report we discuss the technology developed for a swarm of robots searching and assisting fire fighters. We explain the swarming algorithms which provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also one of the means to locate the robots and humans. Thus the robot swarm is able to locate itself and provide guidance information to the humans. Together with the re ghters we explored how the robot swarm should feed information back to the human fire fighter. We have designed and experimented with interfaces for presenting swarm based information to human beings

Acoustic Trilateration Search and Rescue Using Swarm Robotics

The goal of the project is to design and build a robotic system able to locate a sound source via trilateration between multiple mobile robots on the same field, overseen by a master controller. As they navigate the test environment, these robots will demonstrate time difference of arrival (TDOA)-based localization and communication over a wireless network. Although previous research has proven that sound localization is possible on a mobile platform, this concept has not yet been shown for multiple mobile units that must communicate between each other. The intended application of this system is to model robot-aided search-and-rescue or underwater sound mapping

Acoustic SLAM based on the Direction-of-Arrival and the Direct-to-Reverberant Energy Ratio

This paper proposes a new method that fuses acoustic measurements in the reverberation field and low-accuracy inertial measurement unit (IMU) motion reports for simultaneous localization and mapping (SLAM). Different from existing studies that only use acoustic data for direction-of-arrival (DoA) estimates, the source's distance from sensors is calculated with the direct-to-reverberant energy ratio (DRR) and applied as a new constraint to eliminate the nonlinear noise from motion reports. A particle filter is applied to estimate the critical distance, which is key for associating the source's distance with the DRR. A keyframe method is used to eliminate the deviation of the source position estimation toward the robot. The proposed DoA-DRR acoustic SLAM (D-D SLAM) is designed for three-dimensional motion and is suitable for most robots. The method is the first acoustic SLAM algorithm that has been validated on a real-world indoor scene dataset that contains only acoustic data and IMU measurements. Compared with previous methods, D-D SLAM has acceptable performance in locating the robot and building a source map from a real-world indoor dataset. The average location accuracy is 0.48 m, while the source position error converges to less than 0.25 m within 2.8 s. These results prove the effectiveness of D-D SLAM in real-world indoor scenes, which may be especially useful in search and rescue missions after disasters where the environment is foggy, i.e., unsuitable for light or laser irradiation

Robot Localization Using Visual Image Mapping

One critical step in providing the Air Force the capability to explore unknown environments is for an autonomous agent to be able to determine its location. The calculation of the robot\u27s pose is an optimization problem making use of the robot\u27s internal navigation sensors and data fusion of range sensor readings to find the most likely pose. This data fusion process requires the simultaneous generation of a map which the autonomous vehicle can then use to avoid obstacles, communicate with other agents in the same environment, and locate targets. Our solution entails mounting a Class 1 laser to an ERS-7 AIBO. The laser projects a horizontal line on obstacles in the AIBO camera\u27s field of view. Range readings are determined by capturing and processing multiple image frames, resolving the laser line to the horizon, and extract distance information to each obstacle. This range data is then used in conjunction with mapping a localization software to accurately navigate the AIBO

Towards real-time 3D sound sources mapping with linear microphone arrays

© 2017 IEEE. In this paper, we present a method for real-time 3D sound sources mapping using an off-the-shelf robotic perception sensor equipped with a linear microphone array. Conventional approaches to map sound sources in 3D scenarios use dedicated 3D microphone arrays, as this type of arrays provide two degrees of freedom (DOF) observations. Our method addresses the problem of 3D sound sources mapping using a linear microphone array, which only provides one DOF observations making the estimation of the sound sources location more challenging. In the proposed method, multi hypotheses tracking is combined with a new sound source parametrisation to provide with a good initial guess for an online optimisation strategy. A joint optimisation is carried out to estimate 6 DOF sensor poses and 3 DOF landmarks together with the sound sources locations. Additionally, a dedicated sensor model is proposed to accurately model the noise of the Direction of Arrival (DOA) observation when using a linear microphone array. Comprehensive simulation and experimental results show the effectiveness of the proposed method. In addition, a real-time implementation of our method has been made available as open source software for the benefit of the community

Acoustic SLAM

An algorithm is presented that enables devices equipped with microphones, such as robots, to move within their environment in order to explore, adapt to and interact with sound sources of interest. Acoustic scene mapping creates a 3D representation of the positional information of sound sources across time and space. In practice, positional source information is only provided by Direction-of-Arrival (DoA) estimates of the source directions; the source-sensor range is typically difficult to obtain. DoA estimates are also adversely affected by reverberation, noise, and interference, leading to errors in source location estimation and consequent false DoA estimates. Moroever, many acoustic sources, such as human talkers, are not continuously active, such that periods of inactivity lead to missing DoA estimates. Withal, the DoA estimates are specified relative to the observer's sensor location and orientation. Accurate positional information about the observer therefore is crucial. This paper proposes Acoustic Simultaneous Localization and Mapping (aSLAM), which uses acoustic signals to simultaneously map the 3D positions of multiple sound sources whilst passively localizing the observer within the scene map. The performance of aSLAM is analyzed and evaluated using a series of realistic simulations. Results are presented to show the impact of the observer motion and sound source localization accuracy

Developing a Home Service Robot Platform for Smart Homes

The purpose of this work is to develop a testbed for a smart home environment integrated with a home service robot (ASH Testbed) as well as to build home service robot platforms. The architecture of ASH Testbed was proposed and implemented based on ROS (Robot Operating System). In addition, two robot platforms, ASCCHomeBots, were developed using an iRobot Create base and a Pioneer base. They are equipped with capabilities such as mapping, autonomous navigation. They are also equipped with the natural human interfaces including hand-gesture recognition using a RGB-D camera, online speech recognition through cloud computing services provided by Google, and local speech recognition based on PocketSphinx. Furthermore, the Pioneer based ASCCHomeBot was developed along with an open audition system. This allows the robot to serve the elderly living alone at home. We successfully implemented the software for this system that realizes robot services and audition services for high level applications such as telepresence video conference, sound source position estimation, multiple source speech recognition, and human assisted sound classification. Our experimental results validated the proposed framework and the effectiveness of the developed robots as well as the proposed testbed.Electrical Engineerin