Wall-Corner Classification Using Sonar: A New Approach Based on Geometric Features

Ultrasonic signals coming from rotary sonar sensors in a robot gives us several features about the environment. This enables us to locate and classify the objects in the scenario of the robot. Each object and reflector produces a series of peaks in the amplitude of the signal. The radial and angular position of the sonar sensor gives information about location and their amplitudes offer information about the nature of the surface. Early works showed that the amplitude can be modeled and used to classify objects with very good results at short distances—80% average success in classifying both walls and corners at distances less than 1.5 m. In this paper, a new set of geometric features derived from the amplitude analysis of the echo is presented. These features constitute a set of characteristics that can be used to improve the results of classification at distances from 1.5 m to 4 m. Also, a comparative study on classification algorithms widely used in pattern recognition techniques has been carried out for sensor distances ranging between 0.5 to 4 m, and with incidence angles ranging between 20° to 70°. Experimental results show an enhancement on the success in classification rates when these geometric features are considered

Dynamic gridmaps: comparing building techniques

Mobile robots need to represent obstacles in their surroundings, even moving ones, to make right movement decisions. For higher autonomy the robot should automatically build such representation from its sensory input. This paper compares the dynamic character of several gridmap building techniques: probabilistic, fuzzy, theory of evidence and histogramic. Two criteria are defined to rank such dynamism in the representation: time to show a new obstacle and time to show a new hole. The update rules for first three such techniques hold associative property which confers them static character, inconvenient for dynamic environments. Major contribution of this paper is the introduction of two new approaches are presented to improve the perception of mobile obstacles: one uses a differential equation to update the map and another uses majority voting in a limited memory per cell. Their dynamisms are also evaluated and the results presented

Dynamic gridmaps: comparing building techniques

P. 5-22Mobile robots need to represent obstacles in their surroundings, even moving ones, to make right movement decisions. For higher autonomy the robot should automatically build such representation from its sensory input. This paper compares the dynamic character of several gridmap building techniques: probabilistic, fuzzy, theory of evidence and histogramic. Two criteria are defined to rank such dynamism in the representation: time to show a new obstacle and time to show a new hole. The update rules for first three such techniques hold associative property which confers them static character, inconvenient for dynamic environments. Major contribution of this paper is the introduction of two new approaches are presented to improve the perception of mobile obstacles: one uses a differential equation to update the map and another uses majority voting in a limited memory per cell. Their dynamisms are also evaluated and the results presentedS

UWB Radar SLAM: an Anchorless Approach in Vision Denied Indoor Environments

LiDAR and cameras are frequently used as sensors for simultaneous localization and mapping (SLAM). However, these sensors are prone to failure under low visibility (e.g. smoke) or places with reflective surfaces (e.g. mirrors). On the other hand, electromagnetic waves exhibit better penetration properties when the wavelength increases, thus are not affected by low visibility. Hence, this paper presents ultra-wideband (UWB) radar as an alternative to the existing sensors. UWB is generally known to be used in anchor-tag SLAM systems. One or more anchors are installed in the environment and the tags are attached to the robots. Although this method performs well under low visibility, modifying the existing infrastructure is not always feasible. UWB has also been used in peer-to-peer ranging collaborative SLAM systems. However, this requires more than a single robot and does not include mapping in the mentioned environment like smoke. Therefore, the presented approach in this paper solely depends on the UWB transceivers mounted on-board. In addition, an extended Kalman filter (EKF) SLAM is used to solve the SLAM problem at the back-end. Experiments were conducted and demonstrated that the proposed UWB-based radar SLAM is able to map natural point landmarks inside an indoor environment while improving robot localization

Experiments on Surface Reconstruction for Partially Submerged Marine Structures

Over the past 10 years, significant scientific effort has been dedicated to the problem of three-dimensional (3-D) surface reconstruction for structural systems. However, the critical area of marine structures remains insufficiently studied. The research presented here focuses on the problem of 3-D surface reconstruction in the marine environment. This paper summarizes our hardware, software, and experimental contributions on surface reconstruction over the past few years (2008–2011). We propose the use of off-the-shelf sensors and a robotic platform to scan marine structures both above and below the waterline, and we develop a method and software system that uses the Ball Pivoting Algorithm (BPA) and the Poisson reconstruction algorithm to reconstruct 3-D surface models of marine structures from the scanned data. We have tested our hardware and software systems extensively in Singapore waters, including operating in rough waters, where water currents are around 1–2 m/s. We present results on construction of various 3-D models of marine structures, including slowly moving structures such as floating platforms, moving boats, and stationary jetties. Furthermore, the proposed surface reconstruction algorithm makes no use of any navigation sensor such as GPS, a Doppler velocity log, or an inertial navigation system.Singapore-MIT Alliance for Research and Technology. Center for Environmental Sensing and Modelin

Biologically Inspired Monocular Vision Based Navigation and Mapping in GPS-Denied Environments

This paper presents an in-depth theoretical study of bio-vision inspired feature extraction and depth perception method integrated with vision-based simultaneous localization and mapping (SLAM). We incorporate the key functions of developed visual cortex in several advanced species, including humans, for depth perception and pattern recognition. Our navigation strategy assumes GPS-denied manmade environment consisting of orthogonal walls, corridors and doors. By exploiting the architectural features of the indoors, we introduce a method for gathering useful landmarks from a monocular camera for SLAM use, with absolute range information without using active ranging sensors. Experimental results show that the system is only limited by the capabilities of the camera and the availability of good corners. The proposed methods are experimentally validated by our self-contained MAV inside a conventional building