Topic
Inertial measurement unit
About: Inertial measurement unit is a research topic. Over the lifetime, 13326 publications have been published within this topic receiving 189083 citations. The topic is also known as: IMU.
Papers published on a yearly basis
Papers
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TL;DR: A novel mechanical-rotation-rig-free calibration procedure based on blind system identification and a Platonic solid printable using a contemporary 3-D printer that estimates the interIMU misalignment and the gain, bias, and sensitivity axis nonorthogonality of the accelerometers.
Abstract: Ultralow-cost single-chip inertial measurement units (IMUs) combined into IMU arrays are opening up new possibilities for inertial sensing. However, to make these systems practical for researchers, ...
50 citations
21 May 2018
TL;DR: In this paper, an estimator based on Extended Kalman Filter (EKF) is developed to fuse UWB ranging measurements with data from onboard sensors including inertial measurement unit (IMU), altimeters and optical flow.
Abstract: In this paper we propose a method to achieve relative positioning and tracking of a target by a quadcopter using Ultra-wideband (UWB) ranging sensors, which are strategically installed to help retrieve both relative position and bearing between the quadcopter and target. To achieve robust localization for autonomous flight even with uncertainty in the speed of the target, two main features are developed. First, an estimator based on Extended Kalman Filter (EKF) is developed to fuse UWB ranging measurements with data from onboard sensors including inertial measurement unit (IMU), altimeters and optical flow. Second, to properly handle the coupling of the target's orientation with the range measurements, UWB based communication capability is utilized to transfer the target's orientation to the quadcopter. Experiments results demonstrate the ability of the quadcopter to control its position relative to the target autonomously in both cases when the target is static and moving.
50 citations
17 Feb 2014
TL;DR: It is demonstrated that foot-mounted inertial navigation with an IMU array is indeed possible and benefitial and directions for further research are given.
Abstract: Ubiquitous and accurate tracking of pedestrians are an enabler for a large range of emerging and envisioned services and capabilites. To track pedestrians in prevailing indoor environments, inertial measurement units (IMUs) may be used to implement foot-mounted inertial navigation. Today emerging ultra-low-cost IMUs are taking a leading role in the advancement of the IMU performance-to-cost boundary. Unfortunately, the performance of these IMUs are still insufficient to allow extended stand-alone tracking. However, the size, price, and power consumption of single-chip ultra-low-cost IMUs makes it possible to combine multiple IMUs on a single PCB, creating an IMU array. The feasibility of such hardware has recently been demonstrated. On the other hand, the actual gain of using such multi-IMU systems in the pedestrian tracking application is unclear. Therefore, based on an in-house developed IMU array, in the article we demonstrate that foot-mounted inertial navigation with an IMU array is indeed possible and benefitial. The error characteristics of the setup and different ways of combining the inertial measurements are studied and directions for further research are given.
50 citations
TL;DR: This paper presents an implementation of computer vision to hold a quadrocopter aircraft in a stable hovering position using a low-cost, consumer-grade, video system and proposes a slight modification of the Kalman filter to account for this delay.
Abstract: The requirement that mobile robots become independent of external sensors, such as GPS, and are able to navigate in an environment by themselves, means that designers have few alternative techniques available. An increasingly popular approach is to use computer vision as a source of information about the surroundings. This paper presents an implementation of computer vision to hold a quadrocopter aircraft in a stable hovering position using a low-cost, consumer-grade, video system. However, such a system is not able to stabilize the aircraft on its own and must rely on a data-fusion algorithm that uses additional measurements from on-board inertial sensors. Special techniques had to be implemented to compensate for the increased delay in the closed-loop system with the computer vision system, i.e., video timestamping to determine the exact delay of the vision system and a slight modification of the Kalman filter to account for this delay. At the end, the validation results of the proposed filtering technique are presented along with the results of an autonomous flight as a proof of the proposed concept.
50 citations
01 Jan 2009
TL;DR: In this paper, a control strategy called optiPilot can cope with take-off and landing using a small set of inexpensive optic flow sensors, which is similar to the one used in this paper.
Abstract: Take-off and landing manoeuvres are critical for MAVs because GPS-based autopilots usually do not perceive distance to the ground or other potential obstacles. In addition, attitude estimates based on inertial sensors are often perturbed by the strong accelerations occurring during launch. This paper shows how our previously developed control strategy, called optiPilot, can cope with take-off and landing using a small set of inexpensive optic flow sensors.
50 citations