Correspondenceless Ego-Motion Estimation Using an IMU

Background:Objective evaluation of patient outcomes has become an essential component of patient management. Along with patient-reported outcomes, performance-based measures (PBMs) such as gait analysis are an important part of this evaluation. The purpose of this study was to evaluate the validity of utilizing a wearable inertial measurement unit (IMU) in an outpatient clinic setting to assess its ability to provide clinically relevant data in patients with altered gait resulting from lower extremity trauma.Methods:Five orthopaedic trauma patients with varying degrees of gait pathologies were compared to 5 healthy control subjects. Kinematic data were simultaneously recorded by the IMU and a gold standard Vicon video motion analysis system (Vicon Motion Systems Ltd, Oxford, UK) during a modified 10-m walk test. Raw data captured by the IMU were directly compared to Vicon data. Additionally, 5 objective gait parameters were compared for controls and the 5 trauma patients.Results:The IMU data streams strongly correlated with Vicon data for measured variables used in the subsequent gait analysis: vertical acceleration, vertical displacement, pitch angular velocity, and roll angular velocity (Pearson r-value > 0.9 for all correlations). Quantitative kinematic data in post-trauma patients significantly differed from control data and correlated with observed gait pathology.Conclusions:When compared to the gold standard motion capture reference system (Vicon), an IMU can reliably and accurately measure clinically relevant gait parameters and differentiate between normal and pathologic gait patterns. This technology is easily integrated into clinical settings, requires minimal time, and represents a performance-based method for quantifiably assessing gait outcomes.Level of Evidence:Diagnostic Level 1.

Study Algorithm of roll, pitch and yaw angular velocity for three-axis gyroscope. the gyro have only one sensitive quality, can sensitize the three axial angular velocity of the rotating carrier at the same time. in the case of gyro's spin angular velocity presence and pitch or yaw angular velocity worked, gyro output signal contain spin, pitch and yaw angular velocity. Through Uniaxial Gravity Accelerometer's application, the roll, pitch and yaw angular velocity for three-axis gyroscope can be demodulated.

A self-powered 3D activity inertial sensor using hybrid sensing mechanisms

Multi-modal sensor fusion has become ubiquitous in the field of vehicle motion estimation. Achieving a consistent sensor fusion in such a set-up demands the precise knowledge of the misalignments between the coordinate systems in which the different information sources are expressed. In ego-motion estimation, even sub-degree misalignment errors lead to serious performance degradation. The present work addresses the extrinsic calibration of a land vehicle equipped with standard production car sensors and an automotive-grade inertial measurement unit (IMU). Specifically, the article presents a method for the estimation of the misalignment between the IMU and vehicle coordinate systems, while considering the IMU biases. The estimation problem is treated as a joint state and parameter estimation problem, and solved using an adaptive estimator that relies on the IMU measurements, a dynamic single-track model as well as the suspension and odometry systems. Additionally, we show that the validity of the misalignment estimates can be assessed by identifying the misalignment between a high-precision INS/GNSS and the IMU and vehicle coordinate systems. The effectiveness of the proposed calibration procedure is demonstrated using real sensor data. The results show that estimation accuracies below 0.1 degrees can be achieved in spite of moderate variations in the manoeuvre execution.

Achieving an innovative and intelligent low cost sensor system for an autonomous mobile robot to navigate and map the unstructured environment is the main aim of this paper. The environment or the map is unstructured in the sense that, the obstacles in the environment are unknown. There are various approaches to this problem, but the focused approach here can give robust performance of low cost sensor based localisation and mapping. The robust performance is quantified by explicit bounds of the position estimate of a mobile robot and to make it to build the environmental map of its surroundings. The mobile robots generally carry dead reckoning sensors such as wheel encoders and inertial sensors (INS), accelerometers and gyroscopes, to measure the acceleration and the angular rate, while the obstacle detection and map-making is done with the low cost sensor such as time-of-flight ultrasonic sensors. The localisation task is achieved by combining the different source of sensor measurements which are complementary and therefore compensate for each other's limitations, so that the resulting performance of the sensor system is better than of its individual components which provides high richness in the mapping task.

Ego-motion estimation is a core task in robotic systems as well as in augmented and virtual reality applications. It is often solved using visual-inertial odometry, which involves using one or more always-on cameras on mobile robots and wearable devices. As consumers increasingly use such devices in their homes and workplaces, which are filled with sensitive details, the role of privacy in such camera-based approaches is of ever increasing importance. In this letter, we introduce the first solution to perform privacy-preserving ego-motion estimation. We recover camera egomotion from an extremely low-resolution monocular camera by estimating dense optical flow at a higher spatial resolution (i.e., 4x super resolution). We propose SRFNet for directly estimating Super-Resolved Flow, a novel convolutional neural network model that is trained in a supervised setting using ground-truth optical flow. We also present a weakly supervised approach for training a variant of SRFNet on real videos where ground truth flow is unavailable. On image pairs with known relative camera orientations, we use SRFNet to predict the auto-epipolar flow that arises from pure camera translation, from which we robustly estimate the camera translation direction. We evaluate our super-resolved optical flow estimates and camera translation direction estimates on the Sintel and KITTI odometry datasets, where our methods outperform several baselines. Our results indicate that robust ego-motion recovery from extremely low-resolution images can be viable when camera orientations and metric scale is recovered from inertial sensors and fused with the estimated translations.

This paper presents a novel orientation estimate approach named inertial guided visual sample consensus (IGVSAC). This method is intentionally designed for capturing the orientation of human body joints in free-living environments. Unlike the traditional visual-based orientation estimation methods, where outliers among putative image-pair correspondences are removed based on hypothesize-and-verify models such as the computationally costly RANSAC, our approach novelly exploits prior motion information (i.e., rotation and translation) deduced from the quick-response inertial measurement unit (IMU) as the initial body pose to assist camera in removing hidden outliers. In addition, our IGVSAC algorithm is able to ensure estimation accuracy even in the presence of a large quantity of outliers, thanks to its capability of rejecting apparent mismatches. The estimated orientation from the visual sensor is, in turn, able to correct long-term IMU drifts. We conducted extensive experiments to verify the effectiveness and robustness of our IGVSAC algorithm. Comparisons with highly accurate VICON and OptiTrack Motion Tracking Systems prove that our orientation estimate system is quite suitable for capturing human body joints.

This paper presents a new method for 3D position and orientation estimation based on the magnetic field generated by a single coil and the fusion of low power magnetometers and inertial sensors. The fixed spatial relationship between the sensors is exploited to solve the ambiguity problem that occurs when only a single coil is employed. In this context a new positioning algorithm is developed and verified by simulations. Using a single coil instead of three coils as common in conventional magnetic field based systems brings a benefit both regarding power consumption and construction size. Moreover, as the proposed system requires no line of sight between source and sensors, it is ideally suited for portable, location-independent applications such as the mobile assessment of a persons' head to torso posture and movement e.g. for the purpose of examining causes for chronic pain in the cervical spine area under conditions of daily life.

A Drift-Free 3D Orientation and Displacement estimation method (DFOD) based on a single inertial measurement unit (IMU) is proposed and validated. Typically, body segment orientation and displacement methods rely on a constant- or zero-velocity point to correct for drift. Therefore, they are not easily applicable to more proximal segments than the foot. DFOD uses an alternative single sensor drift reduction strategy based on the quasi-cyclical nature of many human movements. DFOD assumes that the quasi-cyclical movement occurs in a quasi-2D plane and with an approximately constant cycle average velocity. DFOD is independent of a constant- or zero-velocity point, a biomechanical model, Kalman filtering or a magnetometer. DFOD reduces orientation drift by assuming a cyclical movement, and by defining a functional coordinate system with two functional axes. These axes are based on the mean acceleration and rotation axes over multiple complete gait cycles. Using this drift-free orientation estimate, the displacement of the sensor is computed by again assuming a cyclical movement. Drift in displacement is reduced by subtracting the mean value over five gait cycle from the free acceleration, velocity, and displacement. Estimated 3D sensor orientation and displacement for an IMU on the lower leg were validated with an optical motion capture system (OMCS) in four runners during constant velocity treadmill running. Root mean square errors for sensor orientation differences between DFOD and OMCS were 3.1 ± 0.4° (sagittal plane), 5.3 ± 1.1° (frontal plane), and 5.0 ± 2.1° (transversal plane). Sensor displacement differences had a root mean square error of 1.6 ± 0.2 cm (forward axis), 1.7 ± 0.6 cm (mediolateral axis), and 1.6 ± 0.2 cm (vertical axis). Hence, DFOD is a promising 3D drift-free orientation and displacement estimation method based on a single IMU in quasi-cyclical movements with many advantages over current methods.

Simple visual odometer such as ORB_SLAM2 algorithm has its limitations, in a short time to move fast, the image is blocked, the image features are sparse can not work stability. The inertial measurement unit (IMU) could calculate the trajectory well in a short time, but it was easy to accumulate too much error in a long time. So we used the visual positioning information to estimate the zero deviation of the IMU, reduce the divergence and accumulation error caused by the zero deviation of the IMU. And the IMU can also provide the positioning for the vision when moving fast. In this paper, two localization algorithms are fused by untraceable Kalman filter to form a more robust localization algorithm. According to the motion model of IMU, the pose was obtained by integrating it. In the visual part, fast and effective ORB feature points were selected for feature tracking and the camera pose was solved. Finally, the visual inertial odometer solution propose in this paper was experimented and verified. The comparative experiments are carried out on the TUM data set first, then on the mobile robot. The hardware platform of the experiments was a mobile robot equipped with a camera and IMU, and the experimental environment is a general indoor environment. The effectiveness and reliability of the visual inertial odometer designed in this paper on mobile robot are verified through experiments. Compared with the pure visual odometer, the scheme is stable, faster and practical.

Accurate ego-motion estimation is critical for an autonomous robot. For this task, image-aided inertial navigation is a good solution. In this paper, the applicability and effectiveness of two algorithms for camera/IMU integration are compared under different ego-motion estimation context: the Multi-State Constraint Kalman Filter (MSCKF) and the Multi-View Geometry Constraint Kalman Filter (MVGCKF) based on Tri-focal Tensor Geometry. Both algorithms estimate the motion of a robot with an IMU (Inertial Measurement Unit), while observations of salient features from a monocular camera are used to correct the inertial navigation error in a Kalman filter. In contrast to traditional vision-aided inertial navigation algorithms, such as EKF-SLAM or the sliding window filter, the measurement models of the both two approaches do not include 3D feature information in filter states, in this way the computing complexity are reduced. We present experimental characterizations of the MSCKF and MVGCKF on KITTI dataset. Theoretical analysis and experimental results show that MSCKF is better for the low speed scenarios such as robot and pedestrian navigation, while MVGCKF is a better choice for the high dynamic and high speed environment such as unmanned land vehicles which need high update rate for navigational parameters. MVGCKF diverges rapidly at stationary and low speed state although its computation burden is less than that of MSCKF.

Tracking of surgical instruments is an essential step towards the modernization of the surgical workflow by a comprehensive surgical landscape guidance system (COMPASS). Real-time tracking of a laparoscopic camera used in minimally-invasive surgery is required for applications in surgical workflow documentation, machine learning, image-localization, and intra-operative visualization. In our approach, an inertial measurement unit (IMU) assists the tool tracking in situations when no line-of-sight is available for infrared (IR) based tracking of the laparoscopic camera. The novelty of this approach lies in the localization method adjusted for the laparoscopic visceral surgery, particularly when the line-of-sight is lost. It is based on IMU tracking and the positioning of the trocar entry point. The trocar entry point is the remote center of motion (RCM), reducing degrees of freedom. We developed a method to tackle localization and a real-time tool for position and orientation estimation. The main error sources are given and evaluated in a test scenario. It reveals that for small changes in penetration length (e.g., pivoting), the IMU’s accuracy determines the error.

For a person following mobile robot, identifying the specific host to follow is challenging in presence of other people with similar features in its camera frame or field of view. In this paper we propose the use of Pedestrian Dead-Reckoning using the Inertial Measurement Unit (IMU) data from smartphone of host or user, which is used to localize the user in the map frame of the mobile robot. The movement and orientation of the user is used to calculate the path of the user and used as global targets for the mobile robot to follow. The mobile robot which is doing Simultaneous Localization and Mapping (SLAM), running on the Robot Operating System (ROS) framework follows the user as the user moves while it also implements obstacle avoidance locally. Since the smartphone is uniquely paired with the mobile robot, the ambiguity of identifying the host is also solved.

This study addresses challenges in sensor fusion for accurate and robust joint orientation estimation in human movement analysis using wearable inertial measurement units (IMUs). A magnetometer-free refined Kalman filter (KF) approach is presented and validated to address various indoor environmental constraints and challenges posed by human movement. These include variability in motion and dynamics, as well as magnetic disturbances caused by ferromagnetic materials or electronic interferences. Our proposed approach utilizes a Kalman-filter-based framework that analyzes the accelerometer’s alignment with the Earth’s frame to estimate orientation and correct gyroscope readings, eliminating reliance on magnetometer inputs. The algorithm was tested on both controlled robotic movements and real-world upper-limb-motion-monitoring scenarios. First, a comparative analysis was conducted on the double-stage Kalman filter (DSKF) and complementary filter using the collected robot motion encoder data. The results demonstrated superior performance in orientation estimation, particularly in yaw measurements, where the proposed method significantly improved accuracy. It achieved a lower root mean square error (RMSE = ) and mean absolute error (MAE = ), outperforming both the DSKF and complementary filter approaches. Additionally, the study’s findings were validated against a standard motion capture system, revealing error metrics within generally acceptable ranges ( of the joint range of motion [ROM]) and strong correlation coefficients (). However, some deviations were observed during complex motion cycle intervals, highlighting opportunities for further refinement. These findings suggest that the proposed approach presents a promising alternative for human joint orientation estimation in industrial settings with magnetic distortions.

Visual and inertial sensors, in combination, are able to provide accurate motion estimates and are well suited for use in many robot navigation tasks. However, correct data fusion, and hence overall performance, depends on careful calibration of the rigid body transform between the sensors. Obtaining this calibration information is typically difficult and time-consuming, and normally requires additional equipment. In this paper we describe an algorithm, based on the unscented Kalman filter, for self-calibration of the transform between a camera and an inertial measurement unit (IMU). Our formulation rests on a differential geometric analysis of the observability of the camera—IMU system; this analysis shows that the sensor-to-sensor transform, the IMU gyroscope and accelerometer biases, the local gravity vector, and the metric scene structure can be recovered from camera and IMU measurements alone. While calibrating the transform we simultaneously localize the IMU and build a map of the surroundings, all without additional hardware or prior knowledge about the environment in which a robot is operating. We present results from simulation studies and from experiments with a monocular camera and a low-cost IMU, which demonstrate accurate estimation of both the calibration parameters and the local scene structure.