Showing papers on "Inertial navigation system published in 2011"

A flexible and scalable SLAM system with full 3D motion estimation

Abstract: For many applications in Urban Search and Rescue (USAR) scenarios robots need to learn a map of unknown environments. We present a system for fast online learning of occupancy grid maps requiring low computational resources. It combines a robust scan matching approach using a LIDAR system with a 3D attitude estimation system based on inertial sensing. By using a fast approximation of map gradients and a multi-resolution grid, reliable localization and mapping capabilities in a variety of challenging environments are realized. Multiple datasets showing the applicability in an embedded hand-held mapping system are provided. We show that the system is sufficiently accurate as to not require explicit loop closing techniques in the considered scenarios. The software is available as an open source package for ROS.

Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration

Abstract: 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.

Visual-inertial navigation, mapping and localization: A scalable real-time causal approach

Abstract: We describe a model to estimate motion from monocular visual and inertial measurements. We analyze the model and characterize the conditions under which its state is observable, and its parameters are identifiable. These include the unknown gravity vector, and the unknown transformation between the camera coordinate frame and the inertial unit. We show that it is possible to estimate both state and parameters as part of an on-line procedure, but only provided that the motion sequence is â??rich enoughâ??, a condition that we characterize explicitly. We then describe an efficient implementation of a filter to estimate the state and parameters of this model, including gravity and camera-to-inertial calibration. It runs in real-time on an embedded platform. We report experiments of continuous operation, without failures, re-initialization, or re-calibration, on paths of length up to 30 km. We also describe an integrated approach to â??loop-closureâ??, that is the recognition of previously seen locations and the topological re-adjustment of the traveled path. It represents visual features relative to the global orientation reference provided by the gravity vector estimated by the filter, and relative to the scale provided by their known position within the map; these features are organized into â??locationsâ?? defined by visibility constraints, represented in a topological graph, where loop-closure can be performed without the need to re-compute past trajectories or perform bundle adjustment. The software infrastructure as well as the embedded platform is described in detail in a previous technical report.

Detecting inertial effects with airborne matter-wave interferometry

Abstract: Inertial sensors relying on atom interferometry offer a breakthrough advance in a variety of applications, such as inertial navigation, gravimetry or ground- and space-based tests of fundamental physics These instruments require a quiet environment to reach their performance and using them outside the laboratory remains a challenge Here we report the first operation of an airborne matter-wave accelerometer set up aboard a 0g plane and operating during the standard gravity (1g) and microgravity (0g) phases of the flight At 1g, the sensor can detect inertial effects more than 300 times weaker than the typical acceleration fluctuations of the aircraft We describe the improvement of the interferometer sensitivity in 0g, which reaches 2 x 10-4 ms-2 / \surdHz with our current setup We finally discuss the extension of our method to airborne and spaceborne tests of the Universality of free fall with matter waves

Estimating Three-Dimensional Orientation of Human Body Parts by Inertial/Magnetic Sensing

Abstract: User-worn sensing units composed of inertial and magnetic sensors are becoming increasingly popular in various domains, including biomedical engineering, robotics, virtual reality, where they can also be applied for real-time tracking of the orientation of human body parts in the three-dimensional (3D) space. Although they are a promising choice as wearable sensors under many respects, the inertial and magnetic sensors currently in use offer measuring performance that are critical in order to achieve and maintain accurate 3D-orientation estimates, anytime and anywhere. This paper reviews the main sensor fusion and filtering techniques proposed for accurate inertial/magnetic orientation tracking of human body parts; it also gives useful recipes for their actual implementation.

Wind Field Estimation for Small Unmanned Aerial Vehicles

Abstract: This paper describes a method for estimating wind field (wind velocity, rate of change of wind velocity, and wind gradient) for small and mini unmanned aerial vehicles. The approach uses sensors that are already part of a standard autopilot sensor suite (Global Positioning System, inertial measurement unit, airspeed, and magnetometer). The primary motivation is enabling energy harvesting; a secondary motivation is development of a low-cost atmospheric measurement and sampling system. The paper presents an error analysis and discusses the primary contributions to error in the estimated wind field. Results of Monte Carlo simulations compare predicted errors in wind estimates with actual errors and show the effect of using estimated winds for energy harvesting from gusts.

Real-time metric state estimation for modular vision-inertial systems

Abstract: Single camera solutions - such as monocular visual odometry or mono SLAM approaches - found a wide echo in the community. All the monocular approaches, however, suffer from the lack of metric scale. In this paper, we present a solution to tackle this issue by adding an inertial sensor equipped with a three-axis accelerometer and gyroscope. In contrast to previous approaches, our solution is independent of the underlying vision algorithm which estimates the camera poses. As a direct consequence, the algorithm presented here operates at a constant computational complexity in real time. We treat the visual framework as a black box and thus the approach is modular and widely applicable to existing monocular solutions. It can be used with any pose estimation algorithm such as visual odometry, visual SLAM, monocular or stereo setups or even GPS solutions with gravity and compass attitude estimation. In this paper, we show the thorough development of the metric state estimation based on an Extended Kalman Filter. Furthermore, even though we treat the visual framework as a black box, we show how to detect failures and estimate drifts in it. We implement our solution on a monocular vision pose estimation framework and show the results both in simulation and on real data.

Accurate and reliable soldier and first responder indoor positioning: multisensor systems and cooperative localization

Abstract: A robust, accurate positioning system with seamless outdoor and indoor coverage is a highly needed tool for increasing safety in emergency response and military urban operations. It must be lightweight, small, inexpensive, and power efficient, and still provide meter-level accuracy during extended operations. GPS receivers, inertial sensors, and local radio-based ranging are natural choices for a multisensor positioning system. Inertial navigation with foot-mounted sensors is suitable as the core system in GPS denied environments, since it can yield meter-level accuracies for a few minutes. However, there is still a need for additional supporting sensors to keep the accuracy at acceptable levels during the duration of typical soldier and first responder operations. Suitable aiding sensors are three-axis magnetometers, barometers, imaging sensors, Doppler radars, and ultrasonic sensors. Further more, cooperative positioning, where first responders exchange position and error estimates in conjunction with performing radio based ranging, is deemed a key technology. This article provides a survey on technologies and concepts for high accuracy soldier and first responder positioning systems, with an emphasis on indoor positioning.

Study on Innovation Adaptive EKF for In-Flight Alignment of Airborne POS

Abstract: The Position and Orientation measurement System (POS) is a dedicated Strapdown Inertial Navigation System (SINS)/Global Positioning System (GPS) integrated system for airborne remote sensing. In-flight alignment (IFA) is an effective way to improve the accuracy and speed of initial alignment for an airborne POS. During IFA, the GPS provides the position and velocity references for the SINS, so the alignment accuracy will be degraded by unstable GPS measurements. To improve the alignment accuracy under unstable GPS measurement, an adaptive filtering algorithm of an extended Kalman filter (EKF) combined with innovation-based adaptive estimation is proposed, which introduces the calculated innovation covariance into the computation of the filter gain matrix directly. Then, this innovation adaptive EKF algorithm is used for the IFA of the POS with a large initial heading error. Moreover, it is optimized by blocked matrix multiplication to reduce the computational burden and improve the real-time performance. To validate the proposed algorithm, the car-mounted IFA experiment is carried out for the prototype of the airborne POS (TX-D10) under a turning maneuver, taking Applanix's POS/AV510 as a reference and changing the GPS measurement artificially. The experiment results demonstrate that the proposed algorithm can reach a better alignment accuracy than the EKF under unknown GPS measurement noises.

Absolute geodetic rotation measurement using atom interferometry.

Abstract: We demonstrate a cold-atom interferometer gyroscope which overcomes accuracy and dynamic range limitations of previous atom interferometer gyroscopes. We show how the instrument can be used for precise determination of latitude, azimuth (true north), and Earth's rotation rate. Spurious noise terms related to multiple-path interferences are suppressed by employing a novel time-skewed pulse sequence. Extended versions of this instrument appear capable of meeting the stringent requirements for inertial navigation, geodetic applications of Earth's rotation rate determination, and tests of general relativity.

Coarse Alignment of a Ship's Strapdown Inertial Attitude Reference System Using Velocity Loci

Abstract: This paper presents a novel estimation method for fast initial coarse alignment of a ship's strapdown inertial attitude reference system using only inertial measurement unit (IMU) measurements for quasi-static alignment and IMU measurements with GPS aiding for moving-base alignment. Unlike several current techniques, the presented estimation method is effective with any initial attitude error. The estimator is based on the decomposition of the attitude quaternion into separate Earth motion, inertial rate, and alignment quaternions. The alignment quaternion is estimated using a minimum variance fit between loci of body- and navigation-frame velocity vectors using solutions to Wahba's problem. One set of vectors is derived from time integrals of measured vehicle motions, and the second set is derived from Earth motion and GPS data (when moving). For the case of quasi-static alignment, an algebraic expression for the covariance of the attitude estimate as a function of the variance of navigation-frame velocity disturbances is developed. It is shown that, by averaging and interleaving the velocity vectors, the resulting attitude estimate is improved over sequential sampling techniques. It is further shown, for a maneuvering vessel, that a continuous estimate of the attitude error covariance can be generated from the IMU data. This latter feature allows direct initialization of a follow-on fine-alignment stochastic estimator's covariance matrix. Results are presented for quasi-static alignment using inertial sensors only and for full in-motion alignment using navigation-frame GPS velocity and position aiding.

Model-Aided INS With Sea Current Estimation for Robust Underwater Navigation

Abstract: This paper reports the development and experimental evaluation of a state-of-the-art model-aided inertial navigation system (MA-INS) for underwater vehicles. Together with real-time sea current estimation, the output from an experimentally validated kinetic vehicle model is integrated in the navigation system to provide velocity aiding for the INS. Additional aiding sources include ultrashort base line (USBL) acoustic positioning, pressure readings, and measurements from a Doppler velocity log (DVL) with bottom track. The performance of the MA-INS is evaluated on data from a field-deployed autonomous underwater vehicle (AUV). Several scenarios are examined, including removal or dropouts of USBL and DVL. The presented results verify that with merely an addition of software and no added instrumentation, it is possible to significantly improve the precision and robustness of an INS by utilizing the physical insight provided by a kinetic vehicle model. To the best of our knowledge, this paper reports the first experimental evaluation and practical application of a MA-INS for underwater vehicle navigation. The proposed approach improves underwater navigation capabilities both for systems lacking conventional velocity measurements, and for systems where the need for redundancy and integrity is important, e.g., during sensor dropouts or failures, or in case of emergency navigation. The MA-INS also represents a feasible step toward the solution to several prospective challenges in underwater navigation, including improved navigation in the midwater zone and increased level of autonomy and robustness.

Intercept-Angle Guidance

Abstract: interest is aerial interception between a missile and a maneuvering target. The guidance concept is applicable in all aerial interception geometries: namely, head-on, tail-chase, and the novel head-pursuit. Analytical conditions for existence of these different engagement geometries are derived. The guidance concept is implemented using the sliding-modeapproach. Thecommonassumption of flight alonganinitial collisiontriangle isnottaken,andthusthe guidance law is applicable for both midcourse and endgame guidance. The application in the different engagement geometries is studied via simulation. It is shown that the head-on scenario allows the smallest range of intercept angles.Italsoplacesthemostseveremaneuverabilityrequirementsontheinterceptor.Thus,insomecases,tail-chase or head-pursuit engagements should be considered instead. The choice between the two is dependent on the adversary’s speed ratio; for tail-chase, the interceptor must have a speed advantage over its target, while for headpursuit, it must have a speed disadvantage.

Strap-down Pedestrian Dead-Reckoning system

Abstract: This paper presents a waist-worn Pedestrian Dead Reckoning (PDR) System that requires minimal end-user calibration. The PDR system is based on an Inertial Measurement Unit (IMU) comprising of a tri-axial accelerometer, a tri-axial magnetometer and a tri-axial gyroscope. We propose a novel heading estimation scheme using a quaternion-based extended Kalman filter (EKF) that estimates magnetic disturbances and corrects for them. Accelerometer measurements are used to detect step events and to estimate step lengths. Experimental results show that a relative distance error of about 3% to 8% can be obtained using our methods.

Indoor navigation with foot-mounted strapdown inertial navigation and magnetic sensors

Abstract: This article describes a method of navigation for an individual based on traditional inertial navigation system (INS) technology, but with very small and self-contained sensor systems. A conventional INS contains quite accurate, but large and heavy, gyroscopes and accelerometers, and converts the sensed rotations and accelerations into position displacements through an algorithm known as a strapdown navigator. They also, almost without exception, use an error compensation scheme such as a Kalman filter to reduce the error growth in the inertially sensed motion through the use of additional position and velocity data from GPS receivers, other velocity sensors (e.g., air, water, and ground speed), and heading aids such as a magnetic compass. This technology has been successfully used for decades, yet the size, weight, and power requirements of sufficiently accurate inertial systems and velocity sensors have prevented their adoption for personal navigation systems. Now, however, as described in this article, miniature inertial measurement units (IMUs) as light as a few grams are available. When placed on the foot to exploit the brief periods of zero velocity when the foot strikes the ground (obviating the need for additional velocity measurement sensors), these IMUs allow the realization of a conventional Kalman-filter-based aided strapdown inertial navigation system in a device no larger or heavier than a box of matches. A particular advantage of this approach is that no stride modeling is involved with its inherent reliance on the estimation of a forward distance traveled on every step — the technique works equally well for any foot motion, something especially critical for soldiers and first responders. Also described is a technique to exploit magnetic sensor orientation data even in indoor environments where local disturbances in the Earth’s magnetic field are significant. By carefully comparing INSderived and magnetically derived heading and orientation, a system can automatically deter

Sensor Fusion and Calibration of Inertial Sensors, Vision, Ultra-Wideband and GPS

Abstract: The usage of inertial sensors has traditionally been confined primarily to the aviation and marine industry due to their associated cost and bulkiness. During the last decade, however, inertial sen ...

Indoor navigation with foot-mounted strapdown inertial navigation and magnetic sensors [Emerging Opportunities for Localization and Tracking]

Abstract: This article describes a method of navigation for an individual based on traditional inertial navigation system (INS) technology, but with very small and self-contained sensor systems. A conventional INS contains quite accurate, but large and heavy, gyroscopes and accelerometers, and converts the sensed rotations and accelerations into position displacements through an algorithm known as a strapdown navigator. They also, almost without exception, use an error compensation scheme such as a Kalman filter to reduce the error growth in the inertially sensed motion through the use of additional position and velocity data from GPS receivers, other velocity sensors (e.g., air, water, and ground speed), and heading aids such as a magnetic compass. This technology has been successfully used for decades, yet the size, weight, and power requirements of sufficiently accurate inertial systems and velocity sensors have prevented their adoption for personal navigation systems. Now, however, as described in this article, miniature inertial measurement units (IMUs) as light as a few grams are available. When placed on the foot to exploit the brief periods of zero velocity when the foot strikes the ground (obviating the need for additional velocity measurement sensors), these IMUs allow the realization of a conventional Kalman-filter-based aided strapdown inertial navigation system in a device no larger or heavier than a box of matches. A particular advantage of this approach is that no stride modeling is involved with its inherent reliance on the estimation of a forward distance traveled on every step ��� the technique works equally well for any foot motion, something especially critical for soldiers and first responders. Also described is a technique to exploit magnetic sensor orientation data even in indoor environments where local disturbances in the Earth���s magnetic field are significant. By carefully comparing INSderived and magnetically derived heading and orientation, a system can automatically determine when sensed magnetic heading is accurate enough to be useful for additional error compensation.

A dead reckoning localization system for mobile robots using inertial sensors and wheel revolution encoding

Abstract: Inertial navigation systems (INS) are composed of inertial sensors, such as accelerometers and gyroscopes. An INS updates its orientation and position automatically; it has an acceptable stability over the short term, however this stability deteriorates over time. Odometry, used to estimate the position of a mobile robot, employs encoders attached to the robot’s wheels. However, errors occur caused by the integrative nature of the rotating speed and the slippage between the wheel and the ground. In this paper, we discuss mobile robot position estimation without using external signals in indoor environments. In order to achieve optimal solutions, a Kalman filter that estimates the orientation and velocity of mobile robots has been designed. The proposed system combines INS and odometry and delivers more accurate position information than standalone odometry.

A review of ground vehicle dynamic state estimations utilising GPS/INS

Abstract: This paper provides an extensive overview of existing vehicle dynamic state estimators that utilise the in-car Inertial Navigation Sensors and Global Positioning System The different approaches are categorised, the techniques are summarised and the limitations and advantages of each approach are provided Recommendations for future research are also given The review is intended to be fairly comprehensive, but with an emphasis on vehicle sideslip estimation

Decentralised cooperative localisation for heterogeneous teams of mobile robots

Abstract: This paper presents a distributed algorithm for performing joint localisation of a team of robots. The mobile robots have heterogeneous sensing capabilities, with some having high quality inertial and exteroceptive sensing, while others have only low quality sensing or none at all. By sharing information, a combined estimate of all robot poses is obtained. Inter-robot range-bearing measurements provide the mechanism for transferring pose information from well-localised vehicles to those less capable. In our proposed formulation, high frequency egocentric data (e.g., odometry, IMU, GPS) is fused locally on each platform. This is the distributed part of the algorithm. Inter-robot measurements, and accompanying state estimates, are communicated to a central server, which generates an optimal minimum mean-squared estimate of all robot poses. This server is easily duplicated for full redundant decentralisation. Communication and computation are efficient due to the sparseness properties of the information-form Gaussian representation. A team of three indoor mobile robots equipped with lasers, odometry and inertial sensing provides experimental verification of the algorithms effectiveness in combining location information.

Relative Navigation Between Two Spacecraft Using X-ray Pulsars

Abstract: This paper suggests utilizing X-ray pulsars for relative navigation between two spacecraft in deep space. Mathematical models describing X-ray pulsar signals are presented. The pulse delay estimation problem is formulated, and the Cramer-Rao lower bound (CRLB) for estimation of the pulse delay is given. Two different pulse delay estimators are introduced, and their asymptotic performance is studied. Numerical complexity of each delay estimator, and the effect of absolute velocity errors on its performance is investigated. Using the pulsar measurements, a recursive algorithm is proposed for relative navigation between two spacecraft. The spacecraft acceleration data are provided by the inertial measurement units (IMUs). The pulse delay estimates are used as measurements, and based on models of the spacecraft and IMU dynamics, a Kalman filter is employed to obtain the 3-D relative position and velocity. Furthermore, it is shown that the relative accelerometer biases as well as the differential time between clocks can be estimated. Numerical simulations are also performed to assess the proposed navigation algorithm.

Rapid Fine Strapdown INS Alignment Method under Marine Mooring Condition

Abstract: In order to solve the strapdown inertial navigation system (INS) alignment problem under the marine mooring condition, the rapid strapdown INS fine alignment method is proposed. This method uses the gravity in the inertial frame to deal with the lineal and angular disturbances. Also the forward and backward processes for strapdown INS calculation and filter estimation are designed. Making use of the forward and backward processes to repeatedly process the saved inertial measurement unit (IMU) data sequence could quickly obtain the initial strapdown attitude matrix. Simulation and trial test validate the performance of the proposed rapid fine strapdown INS alignment.

Enhanced MEMS-IMU/odometer/GPS integration using mixture particle filter

Abstract: Dead reckoning techniques such as inertial navigation and odometry are integrated with GPS to avoid interruption of navigation solutions due to lack of visible satellites. A common method to achieve a low-cost navigation solution for land vehicles is to use a MEMS-based inertial measurement unit (IMU) for integration with GPS. This integration is traditionally accomplished by means of a Kalman filter (KF). Due to the significant inherent errors of MEMS inertial sensors and their time-varying changes, which are difficult to model, severe position error growth happens during GPS outages. The positional accuracy provided by the KF is limited by its linearized models. A Particle filter (PF), being a nonlinear technique, can accommodate for arbitrary inertial sensor characteristics and motion dynamics. An enhanced version of the PF, called Mixture PF, is employed in this paper. It samples from both the prior importance density and the observation likelihood, leading to an improved performance. Furthermore, in order to enhance the performance of MEMS-based IMU/GPS integration during GPS outages, the use of pitch and roll calculated from the longitudinal and transversal accelerometers together with the odometer data as a measurement update is proposed in this paper. These updates aid the IMU and limit the positional error growth caused by two horizontal gyroscopes, which are a major source of error during GPS outages. The performance of the proposed method is examined on road trajectories, and results are compared to the three different KF-based solutions. The proposed Mixture PF with velocity, pitch, and roll updates outperformed all the other solutions and exhibited an average improvement of approximately 64% over KF with the same updates, about 85% over KF with velocity updates only, and around 95% over KF without any updates during GPS outages.

Fiber-optic gyroscopes: from design to production [Invited]

Abstract: Fiber-optic gyroscopes (FOGs) represent an important development in the field of inertial sensors and are now considered an alternative technology to mechanical and ring laser gyroscopes for inertial navigation and control applications. The past 30 years of research and development around the world have established the FOG as a critical sensor for high-accuracy inertial navigation systems. In this paper, specifications, system configurations, and error and sensitivity analysis for different types of FOGs, including critical technology, are presented. This paper addresses the design and production of a different FOG that fulfills requirements such as small size, low production cost, low power consumption, and a broad spectrum of applications.

Mobile Mapping: An Emerging Technology for Spatial Data Acquisition

Abstract: Mobile mapping has been the subject of significant research and develooment bv several research teams over the oust decade. A &bile &upping system consists mainly oia moving platform, navigation sensors, and mapping sensors. The mobile platform may be a land vehicle, a vessel, or an aircraft. Generally, navigation sensors, such as Global Positioning System (GPS) receivers, vehicle wheel sensors, and inertial navigation systems (INS), provide both the track of the vehicle and position and orientation information of the mapping sensors. Objects to be surveyed are sensed d&ectly by mapping sensors, for instance, charge coupled devices (CCD) cameras, laser rangers, and radar sensors. Because the orientation parameters of the mapping sensors are estimated directly by the navigation sensors, complicated computations such as photogrammetric triangulation are greatly simplified or avoided. Spatial information of the objects is extracted directly from the georeferenced mapping sensor data by integrating navigation sensor data. Mobile mapping technology has evolved to a stage which allows mapping and GIS industries to apply it in order to obtain high flexibility in data acquisition, more information with less time and effort, and high productivity. In addition, a successful extension of this technology to helicopter-borne and airborne systems will provide a powerful tool for large-scale and medium-scale spatial data acquisition and database updating. This paper provides a systematic introduction to the use of mobile mapping technology for spatial data acquisition. Issues related to the basic principle, data processing, automation, achievable accuracies, and a break down of errors are given. Application considerations and application examples of the technology in highway and utility mapping are described. Finally, the perspective of the mobile mapping technology is discussed.

RFID-assisted assembly guidance system in an augmented reality environment

Abstract: RFID technology provides an invisible ‘visibility’ to the end user for tracking and monitoring any objects that have been tagged. Research on the application of RFID in assembly lines for overall production monitoring and control has been reported recently. This paper presents a novel research on implementing the RFID technology in the application of assembly guidance in an augmented reality environment. Aiming at providing just-in-time information rendering and intuitive information navigation, methodologies of applying RFID, infrared-enhanced computer vision, and inertial sensor is discussed in this paper. A prototype system is established, and two case studies are presented to validate the feasibility of the proposed system.

Ground vehicle navigation in harsh urban conditions by integrating inertial navigation system, global positioning system, odometer and vision data

Abstract: Combining GPS/INS/odometer data has been considered one of the most attractive methodologies for ground vehicle navigation. In the case of long GPS signal blockages inherent to complex urban environments, however, the accuracy of this approach is largely deteriorated. To overcome this limitation, this study proposes a novel ground vehicle navigation system that combines INS, odometer and omnidirectional vision sensor. Compared to traditional cameras, omnidirectional vision sensors can acquire much more information from the environment thanks to their wide field of view. The proposed system automatically extracts and tracks vanishing points in omnidirectional images to estimate the vehicle rotation. This scheme provides robust navigation information: specifically by combining the advantages of vision, odometer and INS, we estimate the attitude without error accumulation and at a fast running rate. The accurate rotational information is fed back into a Kalman filter to improve the quality of the INS bridging in harsh urban conditions. Extensive experiments have demonstrated that the proposed approach significantly reduces the accumulation of position, velocity and attitude errors during simulated GPS outages. Specifically, the position accuracy is improved by over 30% during simulated GPS outages.