Low-Cost Static LiDAR–IMU Based Real-Time Object Detection for Indoor Environments

Inertial navigation systems for pedestrians are infrastructure-less and can achieve sub-meter accuracy in the short/medium period. However, when low-cost inertial measurement units (IMU) are employed for their implementation, they suffer from a slowly growing drift between the true pedestrian position and the corresponding estimated position. In this paper we illustrate a novel solution to mitigate such a drift by: a) using only accelerometer and gyroscope measurements (no magnetometers required); b) including the sensor error model parameters in the state vector of an extended Kalman filter; c) adopting a novel soft heuristic for foot stance detection and for zero-velocity updates. Experimental results evidence that our inertial-only navigation system can achieve similar or better performance with respect to pedestrian dead-reckoning systems presented in related studies, although the adopted IMU is less accurate than more expensive counterparts.

AlliedSignal Defense & Space Systems is ‘developing a miniaturized low-cost Inertial Measurement Unit (IMU) with tactical performance for missiles and munitions and for commercial applications. Many technology solutions are available that satisfy performance. This IMU design architecture ias established’ addressing cost as the primary performance parameter and then selecting the best technical performance solution to meet the cost goals. The, salient feature of the IMU is the use of two new inertial sensors designed primarily to significantly reduce cost through the use of micro-machined optoelectronic systems (MOES) for the gyro and’ micromachined electromechanical systems (MEMS) accelerometer technology. The SiMMA, Silicon Micro-Machined Accelerometer, provides the first product launch of tactical and inertial grade precision accelerometers. The SiFORS, Silicon Fiber-Optic Rotation Sensor, is an all silicon integrated optics gyro with fiber-optic sensing coil. ~The focus of theis, paper will be on the SiFORS gyro. In this paper we will be describe the design architecture and error budgets and present validating performance.’ 1. SiFORSTG IMU Introduction ’ AlliedSignal has developed a highly integrated very low cost Inertial Measurement Unit (IMU) based on Silicon integrated optics Fiber Optics Rotation Sensor’ (SiFORS) using MOES, Micro-machined OpticalElectronic Systems, and MEMS accelerometer technologies. The SiFORS IMU is environmentally robust and is currently, used in gun-hardened applications. The SiFORS lMU provides 600 Hz ,body rate and acceleration autopilot information and generates accurate. 100 Hi ‘measurements of angle (A@) and velocity (AV) for navigation applications. Its programmable data format and scaling capabilities allow a wide range of inertial navigation applications with minimum ‘design effort. Extensive performance 1 American Institute of Aeronautics and Astronautics 3 evaluations have been performed and the results are in close agreement with the predicted performance envelope. 1.1. Accelerometers in SiFORS IMU L Many solutions are available that satisfy the role of the accelerometer for all IMU applications and the. modular architecture of the’SiFORS lMU provides the flexibility for accelerometer selection. AlliedSignal’s RRA500 is a good accelerometer choice for the l-5 mg bias stability ‘range and the AlliedSignal SiMMATM, Silicon Micro-Machined Accelerometer, a. developing MEMS device, will soon be available in this performance range to further lower the IMU price. The gun-hardened system uses the Fndevco 7591 that provides, 10 mg performance after 12,000g launch shock. 1.2. Focus on SiFORS Rotation Sensor In this paper, we focus on the development and performance of the SiFORS, Silicon Fiber Optic Rotation Sensor.’ This sensor has a unique design employing the best features of both fiber optics and MOES processing. A key component of the SiFORS rotation sensor is the silicon integrated optics MultiFunction Chip (MFC), which provides the optical ( bench and low noise electronic detection capabilities for this Fiber Optic Gyroscope (FOG). An important feature of FOG technology is its performance scalability. The well understood FOG theory has facilitated performance predictions during ,.’ our SiFORS design and development phase. It also provides a low risk approach to developing performance’grade levels for various applications with the benefit being the lowest cost IMU for each performance grade. Since the entire MFC is fabricated using silicon technology, it can be produced with very high yield and is therefore ‘ideally suited for high volume, .low ‘. Copyright

Commonly used Global Navigation Satellite Systems (GNSS) are inappropriate as Location Based Services (LBS) in indoor environment. Therefore research teams are developing different systems, which can be used as a suitable alternative. One of options is to use Inertial Navigation System (INS) which consists of inertial sensors and mathematic procedures. This concept has been known for a long time, but with arrival of Microelectro Mechanical System (MEMS) INS found wide use. Smartphones with inertial sensors, such as accelerometers and gyroscopes, allow us to use them as input devices for Pedestrian Dead Reckoning (PDR). In this paper we present PDR by using smartphone sensors. They can be classified as low-cost Inertial Measurement Unit (IMU), and have been compared with more precise and expensive Xsens IMU. Accuracy of inertial sensors has increased in the past few years, but they still cannot alone provide proper accuracy because of many negative effects, such as heading drift due to gyroscope bias. Particle Filter (PF) has been successfully used with map constraints to increase the accuracy of proposed location system. Presented results show that low-cost smartphone IMU combined with PF can be applicable as proper navigation system.

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.

Inertial navigation system (INS) pivotal error sources are related to the deterministic errors and random errors of the inertial measurement unit (IMU). Low-cost inertial sensors are distinguished by high noise and significant performance uncertainties. Consequently, errors related to low-cost INS states (position, velocity, and attitudes) are rising rapidly in stand-alone mode. If accurate performance can be achieved with low-cost IMU, the cost of real applications can be reduced and the development of new applications may be made feasible. Errors in gyroscopes (gyro) which are the main building block of IMU play a significant role, while the errors in the accelerometers can be calibrated and compensated with reasonable precision by comparison with the gravity of the earth. So precise modeling of gyro for navigation simulation for missiles and unmanned vehicles is essential. To achieve this goal, firstly an error estimation algorithm is implemented. This algorithm firstly is tested with a simulated signal with a known noise parameter injected in it. Moreover, the calibration of deterministic errors is performed. Furthermore, a study of the autocorrelation function of the sensor outputs is presented, and a method for calculating the frequency response to determine potential stability problems is provided by calculating the cutoff frequency. The typical analysis methods discussed throughout the paper are intended to assist designers to develop or choose gyro that is uniquely adapted to the design application requirements. The development technique is used to characterize a tactical grade Micro-Electro-Mechanical Systems (MEMSIC) IMU, and the test results prove its accuracy comparing to the manufacturing calibration sheet.

INS/GNSS integration is a well known technique in navigation and, in general, in time-Position-Velocity-Attitude (tPVA) trajectory determination. It is also known, that INS and GNSS are complementary and that the key to their integration is the correct calibration of the Inertial Measurement Unit (IMU) sensors, of the IMU's Inertial Sensor Assembly (ISA) system parameters (axes misalignments and center of navigation), and of the IMU-to-GNSS system parameters (eccentricity vectors). Calibration is performed both at the manufacturer's facilities prior to shipping and "on-the-job," i.e., together with navigation. Classical INS calibration has two parts: INS error modeling and actual INS calibration once an INS error model is available (also called INS calibration model) either at the manufaturer's or on-the-job. INS error modeling is about understanding the error features - systematic and random - of an IMU and its result uses to be dynamic models - Stochastic Differential Equations (SDE) and white noise amplitude - of the main error sources like biases, scale factors and misalignment angles. INS calibration is about solving for the unknowns (states) of the SDE calibration model together with the unknown states of the SDE of motion. The quality of INS calibration is dependent on the mission dynamics. In classical INS/GNSS navigation, the INS calibration model is dynamics independent; i.e., SDEs formulas and white noise figures are derived following a number of established procedures and then kept fixed. However, it can be shown, that the error features and the INS error calibration model of an IMU change from mission to mission or, more to the point, from one to another type of dynamics. It is therefore natural, to investigate the relationship between IMU errors and motion dynamics and, once this is understood, use this knowledge to improve the performance of INS/GNSS tPVA trajectory determination. In this paper we present a method for IMU error analysis and its first results. It consists of using a reference IMU (REF) together with the IMU under test (IUT) for the simultaneous measurement of REF and ITU signals. First, we show how the sensor random behavior changes with dynamics for tactical and low-cost grade IMUs with respect a navigation grade one. Second, we introduce a procedure to analyze the error of the dynamic dat. The procedure is based on inertial data analysis tools as the Allan Variance applied to the errors of the target IMU with respect to the reference IMU.

Over the few years civilian and military applications are maneuvering with multi-sensor Unmanned Aerial Vehicles (UAV). The aim of this paper is to improve autonomous UAV positioning by using Inertial measurement unit (IMU) and Global Navigation Satellite System (GNSS) is considered. The positional accuracy of UAV is limited to GNSS signal blockage. The advancements in micro electro mechanical systems (MEMS) helps to design low-cost IMU with meter-level accuracy in positioning. The estimation of attitude, position and velocity measurements are determined from a nine degree of freedom Arduino embedded IMU sensor. The Low-cost IMU sensor has been tested under static condition and then a dynamic movement of sensor is observed under moving base-line conditions on February 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> 2020 at Koneru Lakshmaiah Education Foundation (KLEF). In this paper, covariance filters are implemented to eliminate the gyro bias and accelerometer bias for improving attitude determination of IMU navigation system. The positioning from IMU sensor is derived using standard strap-down inertial navigation system (SINS) formulation. The position error from SINS indicates that IMU sensor is able to perform well with maximum position error of 5 meters and 2.5 meters for static and dynamic environments respectively. The outcome of this work would be useful for integration of navigation sensors using kalman filter based advanced techniques for UAV applications.

For indoor pedestrian navigation with a shoe-mounted inertial measurement unit (IMU), the zero velocity update (ZUPT) technique is implemented to constrain the sensors' error. ZUPT uses the fact that a stance phase appears in each step at zero velocity to correct IMU errors periodically. This paper introduces three main contributions we have achieved based on ZUPT. Since correct stance phase detection is critical for the success of applying ZUPT, we have developed a new approach to detect the stance phase of different gait styles, including walking, running and stair climbing. As the extension of ZUPT, we have proposed a new concept called constant velocity update (CUPT) to correct IMU errors on a moving platform with constant velocity, such as elevators or escalators where ZUPT is infeasible. A closed-loop step-wise smoothing algorithm has also been developed to eliminate discontinuities in the trajectory caused by sharp corrections. Experimental results demonstrate the effectiveness of the proposed algorithms.

An algorithm that uses low-cost inertial measurement units (IMUs) for estimating link angles for floating base robotic platforms is proposed. Each link has four IMUs attached on its surfaces, and an Extended Kalman Filter (EKF) and a Complementary Filter (CF) are used for fusing the sensors' data. The algorithm is validated with a commercial mobile working machine, which consist of six degrees-of-freedom (DOF) wheeled base platform, and a 3-DOF hydraulic anthropomorphic arm. Although there are vibrational disturbances from the machine's diesel engine and deformation of the links themselves, the measured results from the planar motion of a floating base hydraulic arm show that the accuracy of the angle estimation is impressively less than 1 degree in the root mean square (RMS) error.

Localization is the crucial problem for mobile robot navigation. For indoor mobile robot, since a global positioning system (GPS) is incapable, another promising technique to detect the position is the received signal strength indicator (RSSI) from wireless communication. To improve the precision and robustness of mobile unit localization, an inertial measurement unit (IMU) is normally used. In this report, we propose the algorithm for mobile robot localization based on sensor fusion between RSSI from wireless local area network (WLAN) and an IMU. The proposed fusion scheme is based on the extended Kalman filter (EKF). The experiment is conducted by using mobile unit equipped with low-cost IMU and a wireless communication module together with access points to evaluate the performance of our algorithm, and the result is promising.

Drones have gained significant attention across various fields; however, the high cost of advanced models may restrict their widespread use in education and research. This paper investigates the potential of affordable drones by presenting two studies on a specific low-cost model, the DJI Tello, to evaluate systems for autonomous detection, tracking, and trajectory execution. The first study introduces a real-time object detection and tracking system utilizing AI to detect and classify objects from the drone's camera feed. This system guides the drone to track and land on a moving ground robot carrying the target object. The study compares two tracking methods-distance-based and velocity-based-and presents significant results demonstrating the applicability of this drone model for object tracking. The second study enhances the drone's autonomy by integrating an external localization system (OptiTrack) to overcome hardware limitations and testing three trajectory execution algorithms. Results indicate that frequent corrections from OptiTrack significantly improve the precision of the drone's movements. By integrating detection, tracking, and external localization systems, this research demonstrates that low-cost drones like the DJI Tello EDU can perform real-time autonomous tasks effectively. This makes them valuable tools for research and educational settings The code for the experiments and the studies has been released to the community. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>https://github.com/ropertUZ/tello_UZ.

The NewSpace landscape for microlaunchers is highly competitive. The cost is one of the main drivers in this competition. Several microlauncher companies have decided to outsource the GNC sub-system to external providers, procuring low-cost commercial-of-the-shelf solutions. Lately, Astos has been involved in several activities related with GNC for (micro-)launchers. Astos’ work has been focused on providing GNC development tools and flexible and easily reconfigurable GNC software that can be applied to a wide range of launch vehicles. Navigation is one of the main drivers for launcher orbital injection accuracy and it is also one of the main drivers for cost in the GNC sub-system. Usually, this cost is driven by the Inertial Navigation System (INS) or Inertial Measurement Unit (IMU), in which expensive technologies are employed such as ring-laser gyroscopes. In traditional inertial propagation algorithms, errors are accumulated due to integration resulting in a steady divergence of the position and attitude estimate. Therefore, the performance of the navigation mainly depends on the performance of the IMU. This makes an investment into a highly accurate inertial sensors, especially the gyroscope, necessary to meet the navigation requirements. Lately, the use of hybrid navigation solutions that fuse the inertial measurements with Global Navigation Satellite Systems (GNSS) measurements has diminished, in some degree, the reliance of the navigation algorithm on the IMU performance. Although this is true for translational states, as position and velocity, the same cannot be said with the same degree of validity for attitude determination and, more specifically, for roll angle estimation. One possible way to reduce the cost of inertial sensors is by using microelectromechanical systems (MEMS). The advantages of using this technology, apart from the large reduction in cost, are the significant saves in mass and power required to operate these sensors. The disadvantage is clearly the lower performance, especially in high-vibration environments such as encountered in launcher vehicles. Lately, efforts to improve the performance of MEMS navigation solutions were driven by the demand of industries such as the wearables and unmanned aerial systems. This led to the development of solutions that utilize multiple low-cost inertial measurement units. By that, the hardware cost is not gravely increased in exchange for higher navigation accuracy, reliability, and redundancy. In this paper, a navigation algorithm is proposed that incorporates the measurements from multiple low-grade MEMS IMUs. The algorithm implements parallel Extended Kalman Filters (EKF), one for each IMU, combining the inertial measurements with the position, velocity, and time solutions provided by a single GNSS receiver. The algorithm has a loosely coupled architecture and the inertial propagation is regularly reset using the fused estimates of the vehicle’s state and IMU biases (closed-loop implementation). The navigation solution, providing estimates of position, velocity, and attitude, is then a combination of the individual EKF results through weighted means based on the covariance of the individual estimates. This formulation utilizing parallel filters is called in the literature as Federated Kalman Filter architecture. An analysis will be presented relating the performance of the navigation architecture with the number of sensors used and their distribution on the launch vehicle’s body. The assessment is performed with relation to a baseline navigation architecture also implementing a loosely coupled, closed-loop, hybrid structure but employing a single low-grade IMU. The analysis will determine the most promising sensor constellation, based on navigation performance, cost and implementation complexity. The selected multiple IMU solution is then compared with two different navigation architectures: Strapdown inertial propagation utilizing a single high-grade IMU; and hybrid navigation scheme implementing a loosely coupled, closed-loop architecture with a medium grade IMU. The performance evaluation is conducted utilizing simulated measurements retrieved from high-fidelity 6 degrees-of-freedom non-linear simulator of a representative microlauncher scenario. The simulators’ dynamics, kinematics, environment, and sensor modelling are provided by the ASTOS tool. Astos intends to further study the feasibility and performance of the proposed Inertial/GNSS hybrid navigation architecture with multiple IMUs. In the roadmap for raising the algorithm’s technology readiness level, more advanced simulations are foreseen, with improved sensor error modelling and previously unmodelled dynamical effects such as flexibility and sloshing. Additionally, processor- and hardware-in-the-loop tests with real sensor data are planned.

Inertial Measurement Unit (IMU) sensors are the essential components in navigation systems. Micro-Electro-Mechanical System (MEMS) sensors are a less affordable and lightweight IMU. MEMS suffer from larger stochastic errors that accumulate over time, eventually causing drifts in navigation. The integration of Global Navigation Satellite System (GNSS) measurements with IMU data solves the issue of navigation drift and provides accurate navigation. However, this integration falls short in during GNSS outage scenarios, primarily due to persistent IMU errors. This thesis introduces an effective strategy to reduce navigation drifts by rectifying IMU errors using Machine Learning (ML) algorithms such as Light Gradient Boosting Machine (LightGBM) and Categorical Boosting (CatBoost). In contrast to many other methodologies reliant on high-end and expensive IMUs for denoising low-cost MEMS IMUs, this thesis proposes Inverse Kinematics (IK). The proposed method undergoes testing in Loosely coupled and Tightly coupled schemes under varying GNSS outage durations.

Misalignment can be an important error source in the integration of the global positioning system (GPS) and inertial navigation systems. This paper presents car test results on the estimation of alignment errors in the integration of a low-grade inertial measurement unit (IMU) with accurate GPS measurement systems. The car test was conducted with a low-cost solid-state IMU and carrier-phase differential GPS measurement systems. Test results showed that changes in the angular velocity improve the estimation of the lever arm between the GPS antenna and IMU. They also showed that changes in acceleration improve the estimation of the relative attitude between the GPS antenna array and IMU. The lever arm was estimated with a 10-cm error. The relative attitude was estimated with a half-degree error. An iterative scheme was used to improve the estimation of the alignment errors during postprocessing. The scheme was shown to be useful when the test car could not have sufficient changes in motion due to limitations in its path. With the given set of test data, the estimation error decreased as the number of iterations increased.

Precision guidance in navigation systems requires highly accurate, compact, and low cost inertial measurement units (IMUs). The key active guided-wave component of the IMU is the phase modulator. In our approach, electro-optic polymers have been utilized in fabricating low loss phase modulators with low half-wave drive voltage using advanced hybrid waveguide fabrication processes and novel optical integration techniques. However, the interference between the primary wave and the backscatter waves generated by the phase modulator and/or the interference between the two counter-propagating backscatter waves at the detector of the IMU has been a major source of error in this approach. A novel technique was introduced in assessing the error caused by backscatter and an offset waveguide design was developed to suppress the interference of backscatter light. The novel design not only preserved the miniaturization, but also improved the insertion loss with the use of a shorter waveguide. The gyro level tests performed with the backscatter compensated modulators showed about 5 times improvement of the average bias uncertainty over gyros integrated with a standard symmetric phase modulator.