This paper presents a new implementation method for efficient simultaneous localization and mapping using a forward-viewing monocular vision sensor. The method is developed to be applicable in real time on a low-cost embedded system for indoor service robots. In this study, the orientation of a robot is directly estimated using the direction of the vanishing point. Then, the estimation models for the robot position and the line landmark are derived as simple linear equations. Using these models, the camera poses and landmark positions are efficiently corrected by a local map correction method. The performance of the proposed method is demonstrated under various challenging environments using dataset-based experiments using a desktop computer and real-time experiments using a low-cost embedded system. The experimental environments include a real home-like setting. These conditions contain low-textured areas, moving people, or changing environments. The proposed method is also tested using the robotics advancement through web-publishing of sensorial and elaborated extensive data sets (RAWSEEDS) benchmark dataset.

This paper presents a deep neural network (DNN)-based slip ratio estimator fused with an invariant extended Kalman filter (IEKF) for lugged-wheel robot localization using an inertial sensor and an encoder. Among various sensors used in wheeled mobile robot (WMR) localization, inertial sensors and encoders are most commonly used because these sensors are inexpensive and have low computational requirements. However, inertial sensors and encoders can cause large drifts in localization due to inherent sensor characteristics and wheel slippage, respectively. Most studies on wheel slippage have primarily focused on rubber tires, and using this slip ratio model for WMRs with lugged-wheels operating in outdoor environments can result in significant estimation errors in slip ratios. This paper develops a DNN-based slip ratio estimator and IEKF for WMR localization that is robust to wheel slippage even in rugged outdoor environments. The performance of the proposed localization is demonstrated through experiments using outdoor datasets where WMRs with lugged-wheels experience various slip conditions. Experiments are conducted in wet and dry conditions on a sloped grass field. Results show that the proposed localization method reduces accumulated localization errors by 53.5% compared to integration-based localization and by 13.5% compared to IEKF-based localization.

Air flow measurements provide significant information required for understanding the characteristics of insect movement. This study proposes a four-channel low-noise readout integrated circuit (IC) in order to measure air flow (air velocity), which can be beneficial to insect biomimetic robot systems that have been studied recently. Instrumentation amplifiers (IAs) with low-noise characteristics in readout ICs are essential because the air flow of an insect’s movement, which is electrically converted using a microelectromechanical systems (MEMS) sensor, generally produces a small signal. The fundamental architecture employed in the readout IC is a three op amp IA, and it accomplishes low-noise characteristics by chopping. Moreover, the readout IC has a four-channel input structure and implements an automatic offset calibration loop (AOCL) for input offset correction. The AOCL based on the binary search logic adjusts the output offset by controlling the input voltage bias generated by the R-2R digital-to-analog converter (DAC). The electrically converted air flow signal is amplified using a three op amp IA, which is passed through a low-pass filter (LPF) for ripple rejection that is generated by chopping, and converted to a digital code by a 12-bit successive approximation register (SAR) analog-to-digital converter (ADC). Furthermore, the readout IC contains a low-dropout (LDO) regulator that enables the supply voltage to drive digital circuits, and a serial peripheral interface (SPI) for digital communication. The readout IC is designed with a 0.18 μm CMOS process and the current consumption is 1.886 mA at 3.3 V supply voltage. The IC has an active area of 6.78 mm² and input-referred noise (IRN) characteristics of 95.4 nV/√Hz at 1 Hz.

This paper presents a three-axis biomimetic gyroscope, mimicking the haltere of Diptera. Diptera use a club-shaped mechanosensory organ called the haltere to get the three-axis angular velocity information, namely roll, pitch and yaw axes, for flight control. One pair of halteres is physically connected to the wings of Diptera that vibrate in antiphase to the flapping wings in ambient air. They sense the Coriolis force and relay angular velocity information to the Diptera. As an alternative to the conventional micro-electro-mechanical system gyroscopes which are widely used in robotics, many research groups have attempted to mimic the haltere. However, no previous study succeeded in measuring all three-axis components of angular velocity, due to various shortcomings. In this paper, we developed the first three-axis haltere-mimicking gyroscope. Two perpendicularly positioned haltere-mimicking structures that can vibrate at a 180° amplitude were mechanically integrated into a robot actuator. Two accelerometers, placed at the tip of each structure, were employed to measure the Coriolis force. The performance of the novel biomimetic gyroscope was measured in all rotational directions, using a motion capture system as the ground truth. One-axis input experiments were performed 240 times at different input magnitudes and directions, and the measured orientation error was less than ±2.0% in all experiments. In 80 three-axis input experiments, the orientation error was less than ±3.5%.