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In this paper, an EKF (Extended Kalman Filter)-based algorithm is proposed to estimate 3D position and velocity components of different cars in a scene by fusing the semantic information and car model, extracted from successive frames with camera motion parameters. First, a 2D virtual image of the scene is made using a prior knowledge of the 3D Computer Aided Design (CAD) models of the detected cars and their predicted positions. Then, a discrepancy, i.e., distance, between the actual image and the virtual image is calculated. The 3D position and the velocity components are recursively estimated by minimizing the discrepancy using EKF. The experiments on the KiTTi dataset show a good performance of the proposed algorithm with a position estimation error up to 3–5% at 30 m and velocity estimation error up to 1 m/s.

Binocular disparity and motion parallax are the most important cues for depth estimation in human and computer vision. Here, we present an experimental study to evaluate the accuracy of these two cues in depth estimation to stationary objects in a static environment. Depth estimation via binocular disparity is most commonly implemented using stereo vision, which uses images from two or more cameras to triangulate and estimate distances. We use a commercial stereo camera mounted on a wheeled robot to create a depth map of the environment. The sequence of images obtained by one of these two cameras as well as the camera motion parameters serve as the input to our motion parallax-based depth estimation algorithm. The measured camera motion parameters include translational and angular velocities. Reference distance to the tracked features is provided by a LiDAR. Overall, our results show that at short distances stereo vision is more accurate, but at large distances the combination of parallax and camera motion provide better depth estimation. Therefore, by combining the two cues, one obtains depth estimation with greater range than is possible using either cue individually.

We propose a method to estimate the distance to objects based on the complementary nature of monocular image sequences and camera kinematic parameters. The fusion of camera measurements with the kinematics parameters that are measured by an IMU and an odometer is performed using an extended Kalman filter. Results of field experiments with a wheeled robot corroborated the results of the simulation study in terms of accuracy of depth estimation. The performance of the approach in depth estimation is strongly affected by the mutual observer and feature point geometry, measurement accuracy of the observer's motion parameters and distance covered by the observer. It was found that under favorable conditions the error in distance estimation does not exceed 1% of the distance to a feature point. This approach can be used to estimate distance to objects located hundreds of meters away from the camera.

—: We propose a method to estimate the distance to objects based on the complementary nature of monocular image sequences and camera kinematic parameters. The fusion of camera measurements with the kinematics parameters that are measured by an IMU and an odometer is performed using an extended Kalman filter. Results of field experiments with a wheeled robot corroborated the results of the simulation study in terms of accuracy of depth estimation. The performance of the approach in depth estimation is strongly affected by the mutual observer and feature point geometry, measurement accuracy of the observer’s motion parameters and distance covered by the observer. It was found that under favorable conditions the error in distance estimation can be as small as 1% of the distance to a feature point. This approach can be used to estimate distance to objects located hundreds of meters away from the camera. acceptedVersion

Due to the universal presence of motion, vibration, and shock, inertial motion sensors can be applied in various contexts. Development of the microelectromechanical (MEMS) technology opens up many new consumer and industrial applications for accelerometers and gyroscopes. The multiformity of applications creates different requirements to inertial sensors in terms of accuracy, size, power consumption and cost. This makes it challenging to choose sensors that are suited best for the particular application. In addition, development of signal processing algorithms for inertial sensor data require understanding on the physical principles of both motion generated and sensor operation principles. This chapter aims to aid the system designer to understand and manage these challenges. The principles of operation of accelerometers and gyroscopes are explained with examples of different applications using inertial sensors data as input. Especially, detailed examples of signal processing algorithms for pedestrian navigation and motion classification are given. acceptedVersion

This paper presents an approach to navigation system’s position and heading correction using building floor plans. The algorithm includes three steps: (a) autonomous sensors data processing to obtain position and heading, (b) map-matching correction, and (c) navigation system errors estimation. A particle filter is used to incorporate the building plan information and a Kalman filter estimates the dead reckoning error states. This algorithm was designed for vehicle navigation systems operating inside buildings with known floor plans and can be adapted for implementation on real-time navigation systems using low-cost MEMS gyroscope and speed sensor as dead reckoning instruments. The real-world data collected from the vehicle indoor tests has shown that the proposed algorithm is able to correct significant errors in dead reckoning position and heading by applying the map constraints.

This paper provides an overview of the most significant existing methods for indoor positioning on a contemporary smartphone. The approaches include Wi-Fi and Bluetooth based positioning, magnetic field fingerprinting, map aided navigation using building floor plans, and aiding from self-contained sensors. Wi-Fi and Bluetooth based positioning methods considered in this survey are fingerprint approaches that determine a user’s position using a database of radio signal strength measurements that were collected earlier at known locations. Magnetic field fingerprinting can be used in an information fusion algorithm to improve positioning. The map-matching algorithms include application of wall constraints, topological indoor maps, and building geometry for heading correction. Finally, methods for step counting, step length and direction estimation, orientation tracking, motion classification, transit mode detection, and floor change detection in multi-storey buildings are discussed. acceptedVersion

This paper describes an approach for fusion of monocular vision measurements, camera motion, odometer and inertial rate sensor measurements. The motion of the camera between successive images generates a baseline for range computations by triangulation. The recursive estimation algorithm is based on extended Kalman filtering. The depth estimation accuracy is strongly affected by the mutual observer and feature point geometry, measurement accuracy of observer motion parameters and line of sight to a feature point. The simulation study investigates how the estimation accuracy is affected by the following parameters: linear and angular velocity measurement errors, camera noise, and observer path. These results impose requirements to the instrumentation and observation scenarios. It was found that under favorable conditions the error in distance estimation does not exceed 2% of the distance to a feature point. acceptedVersion

This paper presents the numerical probabilistic approach to map-matching problem within the framework of Bayesian theory. The proposed solution is based on sequential Monte Carlo method, so called particle filtering. This algorithm can be adapted for implementation on real-time portable car navigation systems equipped with GPS or dead reckoning sensors. The algorithm reliability and accuracy performance was investigated using simulated data and data from real-world driving tests in urban environment.

The problem of bearings-only target localization is to estimate the location of a fixed target from a sequence of noisy bearing measurements. Although, in theory, this process is observable even without an observer maneuver, estimation performance (i.e., accuracy, stability and convergence rate) can be greatly enhanced by properly exploiting observer motion to increase observability. This work addresses the optimization of observer trajectories for bearings-only fixed-target localization. The approach presented herein is based on maximizing the determinant of the Fisher information matrix (FIM), subject to state constraints imposed on the observer trajectory (e.g., by the target defense system). Direct optimal control numerical schemes, including the recently introduced differential inclusion (DI) method, are used to solve the resulting optimal control problem. Computer simulations, utilizing the familiar Stansfield and maximum likelihood (ML) estimators, demonstrate the enhancement to target position estimability using the optimal observer trajectories.

This paper presents the development of a land vehicle navigation system that provides accurate and uninterrupted positioning. A ground speed Doppler radar and one MEMS gyroscope are used to augment differential GPS (DGPS) and provide accurate navigation during GPS outages. The goal is to maintain a position accuracy of 2 meters or better for 15 seconds when an accurate GPS solution is not available. The Doppler radar and gyro are calibrated when DGPS is available, and a loosely coupled Kalman filter gives an optimally tuned navigation solution. Field tests were carried out in a harbor environment using straddle carriers.

This paper presents the development of car navigation system for portable navigation devices and car telematics applications. The objective was to develop a system that can provide uninterrupted reliable navigation even when GPS signals are not available. The approach uses digital maps, 3D accelerometer and one gyro for directional measurements to improve positioning availability and reliability in weak signal environment and during short GPS signal outages. This system does not require vehicle installation and can be easily transferred between vehicles. Loosely coupled extended Kalman filter and probabilistic map-matching algorithm provide optimally tuned navigation solution and continuous auto calibration of inertial sensors. A real-time prototype was built. The system accuracy performance was investigated using field tests in an urban environment.

This paper presents a novel approach to INS velocity aiding in autonomous pedestrian navigation systems with body-mounted IMU. The proposed solution uses a kinetic model of human gait as a virtual velocity sensor. In this paper we show how an understanding of INS error dynamics and knowledge of human motion help to curb the divergence of INS computed horizontal velocity and tilt errors. Heading and heading gyro drift cannot be corrected with this method and require some additional procedures. This algorithm is based on Kalman filter and can be adapted for implementation on real-time pedestrian navigation systems equipped with 6 DOF IMU. The algorithm accuracy performance was investigated using data from indoor walking tests.

This paper presents the development of a land vehicle navigation system that provides accurate and uninterrupted positioning. Ground speed Doppler radar and one MEMS gyroscope are used to augment differential GPS (DGPS) and provide accurate navigation during DGPS outages. Using Doppler radar has advantages of easy assembling and lowcost maintenance compared to wheel encoders. The Doppler radar and gyro are calibrated when DGPS is available. Loosely coupled Kalman filter gives optimally tuned navigation solution. Field tests were carried out to evaluate the performance of the system. The results show that position accuracy of 1.5 meters can be achieved during 15 seconds DGPS outages.