Inertial measurement unit

About: Inertial measurement unit is a research topic. Over the lifetime, 13326 publications have been published within this topic receiving 189083 citations. The topic is also known as: IMU.

Visual Odometry Algorithm Using an RGB-D Sensor and IMU in a Highly Dynamic Environment

Abstract: This paper proposes a robust visual odometry algorithm using a Kinect-style RGB-D sensor and inertial measurement unit (IMU) in a highly dynamic environment. Based on SURF (Speed Up Robust Features) descriptor, the proposed algorithm generates 3-D feature points incorporating depth information into RGB color information. By using an IMU, the generated 3-D feature points are rotated in order to have the same rigid body rotation component between two consecutive images. Before calculating the rigid body transformation matrix between the successive images from the RGB-D sensor, the generated 3-D feature points are filtered into dynamic or static feature points using motion vectors. Using the static feature points, the rigid body transformation matrix is finally computed by RANSAC (RANdom SAmple Consensus) algorithm. The experiments demonstrate that visual odometry is successfully obtained for a subject and a mobile robot by the proposed algorithm in a highly dynamic environment. The comparative study between proposed method and conventional visual odometry algorithm clearly show the reliability of the approach for computing visual odometry in a highly dynamic environment.

Combining Inertial Navigation and ICP for Real-time 3D Surface Reconstruction

Abstract: We present a novel method to improve the robustness of real-time 3D surface reconstruction by incorporating inertial sensor data when determining inter-frame alignment. With commodity inertial sensors, we can significantly reduce the number of iterative closest point (ICP) iterations required per frame. Our system is also able to determine when ICP tracking becomes unreliable and use inertial navigation to correctly recover tracking, even after significant time has elapsed. This enables less experienced users to more quickly acquire 3D scans. We apply our framework to several different surface reconstruction tasks and demonstrate that enabling inertial navigation allows us to reconstruct scenes more quickly and recover from situations where reconstructing without IMU data produces very poor results.

OxIOD: The Dataset for Deep Inertial Odometry.

Abstract: Advances in micro-electro-mechanical (MEMS) techniques enable inertial measurements units (IMUs) to be small, cheap, energy efficient, and widely used in smartphones, robots, and drones. Exploiting inertial data for accurate and reliable navigation and localization has attracted significant research and industrial interest, as IMU measurements are completely ego-centric and generally environment agnostic. Recent studies have shown that the notorious issue of drift can be significantly alleviated by using deep neural networks (DNNs), e.g. IONet. However, the lack of sufficient labelled data for training and testing various architectures limits the proliferation of adopting DNNs in IMU-based tasks. In this paper, we propose and release the Oxford Inertial Odometry Dataset (OxIOD), a first-of-its-kind data collection for inertial-odometry research, with all sequences having ground-truth labels. Our dataset contains 158 sequences totalling more than 42 km in total distance, much larger than previous inertial datasets. Another notable feature of this dataset lies in its diversity, which can reflect the complex motions of phone-based IMUs in various everyday usage. The measurements were collected with four different attachments (handheld, in the pocket, in the handbag and on the trolley), four motion modes (halting, walking slowly, walking normally, and running), five different users, four types of off-the-shelf consumer phones, and large-scale localization from office buildings. Deep inertial tracking experiments were conducted to show the effectiveness of our dataset in training deep neural network models and evaluate learning-based and model-based algorithms. The OxIOD Dataset is available at: this http URL

Inertial Odometry on Handheld Smartphones

Abstract: Building a complete inertial navigation system using the limited quality data provided by current smartphones has been regarded challenging, if not impossible. This paper shows that by careful crafting and accounting for the weak information in the sensor samples, smartphones are capable of pure inertial navigation. We present a probabilistic approach for orientation and use-case free inertial odometry, which is based on double-integrating rotated accelerations. The strength of the model is in learning additive and multiplicative IMU biases online. We are able to track the phone position, velocity, and pose in realtime and in a computationally lightweight fashion by solving the inference with an extended Kalman filter. The information fusion is completed with zero-velocity updates (if the phone remains stationary), altitude correction from barometric pressure readings (if available), and pseudo-updates constraining the momentary speed. We demonstrate our approach using an iPad and iPhone in several indoor dead-reckoning applications and in a measurement tool setup.

Development of compact cold-atom sensors for inertial navigation

Abstract: Inertial sensors based on cold atom interferometry exhibit many interesting features for applications related to inertial navigation, particularly in terms of sensitivity and long-term stability. However, at present the typical atom interferometer is still very much an experiment—consisting of a bulky, static apparatus with a limited dynamic range and high sensitivity to environmental effects. To be compliant with mobile applications further development is needed. In this work, we present a compact and mobile experiment, which we recently used to achieve the first inertial measurements with an atomic accelerometer onboard an aircraft. By integrating classical inertial sensors into our apparatus, we are able to operate the atomic sensor well beyond its standard operating range, corresponding to half of an interference fringe. We report atom-based acceleration measurements along both the horizontal and vertical axes of the aircraft with one-shot sensitivities of 2.3 × 10−4 g over a range of ∼ 0.1 g. The same technology can be used to develop cold-atom gyroscopes, which could surpass the best optical gyroscopes in terms of long-term sensitivity. Our apparatus was also designed to study multi-axis atom interferometry with the goal of realizing a full inertial measurement unit comprised of the three axes of acceleration and rotation. Finally, we present a compact and tunable laser system, which constitutes an essential part of any cold-atom-based sensor. The architecture of the laser is based on phase modulating a single fiber-optic laser diode, and can be tuned over a range of 1 GHz in less than 200 μs.