Inertial navigation system

About: Inertial navigation system is a research topic. Over the lifetime, 14582 publications have been published within this topic receiving 190618 citations. The topic is also known as: intertial guidance system & inertial reference platform.

IMM-Based Lane-Change Prediction in Highways With Low-Cost GPS/INS

Abstract: The prediction of lane changes has been proven to be useful for collision avoidance support in road vehicles. This paper proposes an interactive multiple model (IMM)-based method for predicting lane changes in highways. The sensor unit consists of a set of low-cost Global Positioning System/inertial measurement unit (GPS/IMU) sensors and an odometry captor for collecting velocity measurements. Extended Kalman filters (EKFs) running in parallel and integrated by an IMM-based algorithm provide positioning and maneuver predictions to the user. The maneuver states Change Lane (CL) and Keep Lane (KL) are defined by two models that describe different dynamics. Different model sets have been studied to meet the needs of the IMM-based algorithm. Real trials in highway scenarios show the capability of the system to predict lane changes in straight and curved road stretches with very short latency times.

Indoor Positioning System Using Accelerometry and High Accuracy Heading Sensors

Abstract: The current GPS signal structure and signal power levels are barely sufficient for indoor applications. Recent developments in high sensitivity receiver technology are promising for indoor positioning inside light structures such as wooden frame houses but generally not for concrete high rise buildings. Errors due to multipath and noise associated with weak indoor signals limit the accuracy and availability of GNSS in difficult indoor environments. An alternate approach makes use of inertial technologies. However, the use of a strapdown inertial navigation system (INS) system and its traditional mechanization as a personal indoor positioning system is rather unrealistic due to the rapidly growing positioning errors caused by gyro drifts. Even a high performance INS will cause hundreds of metres of positioning error in 30 minutes without GPS updates. The majority of previously proposed personal positioning systems utilize the Pedestrian Dead Reckoning (PDR) approach. These systems use accelerometers for step detection and step length estimation and magnetic compasses or low cost gyros for heading determination. In such systems, the error sources are the step length estimation error and the heading error. Assuming no heading error, the positioning error is directly proportional to the number of steps and, thus, to the distance traveled. However, the critical component of these systems is heading. Indoor, apart from measuring the Earth's magnetic field, magnetic sensors will be subject to other local electromagnetic fields. Over time, low cost gyros will drift in a significant and unpredictable manner which makes them unsuitable for obtaining adequate heading information. In this paper, the heading problem is resolved by using one deg/hour ring laser gyros. A tactical-grade IMU is used to provide accurate heading information, and accelerometers are used only for step occurrence detection. A special study is carried out to examine errors in heading, if only one gyro is used instead of three. In this case, the three-gyro solution is used as a reference. To test the concept, several long period tests were performed, carrying the IMU in a backpack. Both DGPS and stand-alone receivers were included to compare two different level initialization sources. 3D-gyro heading solutions initialized with DGPS are promising. However, if only one gyro is used or DGPS is not available, heading solution accuracy degrades significantly. Size restrictions on current ring laser gyros limit the application of the proposed system. However, as gyro technology evolves, such a system may be beneficial for applications such as the positioning of rescue workers, police squads, and other indoor location and navigation applications.

A new approach to vision-aided inertial navigation

Abstract: We combine a visual odometry system with an aided inertial navigation filter to produce a precise and robust navigation system that does not rely on external infrastructure. Incremental structure from motion with sparse bundle adjustment using a stereo camera provides real-time highly accurate pose estimates of the sensor which are combined with six degree-of-freedom inertial measurements in an Extended Kalman Filter. The filter is structured to neatly handle the incremental and local nature of the visual odometry measurements and to handle uncertainties in the system in a principled manner. We present accurate results from data acquired in rural and urban scenes on a tractor and a passenger car travelling distances of several kilometers.

Comparison and evaluation of acceleration based step length estimators for handheld devices

Abstract: A comparison between different step length estimation algorithms for pedestrian dead reckoning is presented. This work covers theoretic evaluation of the estimators' performance and presents a comparison based on measurement data. Measurement data were taken from a group of five adults walking at three different velocities. For reference, the sensors were placed according to the recommendation given for each algorithm. In respect to everyday usability the performance of the estimators is furthermore evaluated for arbitrary placement of the sensors, as it is the case when using a mobile measurement platform like a smartphone.

Tightly-Coupled Integration of WiFi and MEMS Sensors on Handheld Devices for Indoor Pedestrian Navigation

Abstract: The need for indoor pedestrian navigators is quickly increasing in various applications over the last few years. However, indoor navigation still faces many challenges and practical issues, such as the need for special hardware designs and complicated infrastructure requirements. This paper originally proposes a pedestrian navigator based on tightly coupled (TC) integration of low-cost microelectromechanical systems (MEMS) sensors and WiFi for handheld devices. Two other approaches are proposed in this paper to enhance the navigation performance: 1) the use of MEMS solution based on pedestrian dead reckoning/inertial navigation system (PDR/INS) integration and 2) the use of motion constraints, such as non-holonomic constraints, zero velocity update, and zero angular rate update for the MEMS solution. There are two main contributions in this paper: 1) TC fusion of WiFi, INS, and PDR for pedestrian navigation using an extended Kalman filter and 2) better heading estimation using PDR and INS integration to remove the gyro noise that occurs when only vertical gyroscope is used. The performance of the proposed navigation algorithms has been extensively verified through field tests in indoor environments. The experiment results showed that the average root mean square position error of the proposed TC integration solution was 3.47 m in three trajectories, which is 0.01% of INS, 10.38% of PDR, 32.11% of the developed MEMS solution, and 64.58% of the loosely coupled integration. The proposed TC integrated navigation system can work well in the environment with sparse deployment of WiFi access points.