A New Inertial Sensing System for Dynamic Measurement of Parallel Machines

One way to solve the problem of measurement precision caused by deformity for thermal expansion, friction and load etc is to use an inertial sensor to measure a change in the length of the rod on a parallel machine. However, the characteristic of dynamic measurement in the inertial sensing system and the effects of the machine＇s working environment, bias error, misalignment and wide band random noise in inertial measurement data results in the in-accuracy of system measurement. Therefore, on the basis of the measurement system a new inertial sensing system is proposed; the drifting of error is restrained with a method of inertial error correction and the system＇s position and the velocity state variables are predicted by the data fusion. After measuring the whole 300mm movement in an experiment, the analyses of the experimental result showed that the application of the new inertial sensing system can improve the positional accuracy about 61% and the movement precision more than 20%. Measurement results also showed that the application of the new inertial sensing system for dynamic measurement was a feasible method to improve the machine＇s dynamic positioning precision. And with the further improvement of the low-cost solid-stateacceleramenter technology, the application of the machine can take a higher position and make the speed dynamic accuracy possible.

Fast and accurate visual odometry from a monocular camera

This paper aims at a semi-dense visual odometry system that is accurate,robust,and able to run realtime on mobile devices,such as smartphones,AR glasses and small drones.The key contributions of our system include:1)the modified pyramidal Lucas-Kanade algorithm which incorporates spatial and depth constraints for fast and accurate camera pose estimation;2)adaptive image resizing based on inertial sensors for greatly accelerating tracking speed with little accuracy degradation;and 3)an ultrafast binary feature description based directly on intensities of a resized and smoothed image patch around each pixel that is sufficiently effective for relocalization.A quantitative evaluation on public datasets demonstrates that our system achieves better tracking accuracy and up to about 2X faster tracking speed comparing to the state-of-the-art monocular SLAM system:LSD-SLAM.For the relocalization task,our system is 2.0X∼4.6X faster than DBoW2 and achieves a similar accuracy.

A gyroscope-based system for intraoperative measurement of tibia coronal plane alignment in total knee arthroplasty

Coronal plane alignment in total knee arthroplasty(TKA)is an important predictor of clinical outcomes including patient satisfaction and device longevity.Radiography and computer assisted navigation are the two primary technologies currently available to surgeons for intraoperative assessment of alignment;however,neither is particularly well-suited for use in this increasingly high volume procedure.Herein we propose a novel gyroscopebased instrument for intraoperative validation of tibia coronal plane alignment,and provide initial analytical and experimental performance assessments.The gyroscope-based alignment estimate is derived from simplified joint geometry and verified experimentally using a custom tibial trial insert containing a consumer-grade inertial measurement unit(IMU).Average accuracy of the gyroscope-based tibia coronal angle estimate was found to be within
1in mechanical leg jig and cadaver testing.These results indicate that the proposed gyroscope-based method shows promise for low cost,accurate intraoperative validation of limb alignment in TKA patients.Integrating IMU technology into the TKA surgical workflow via low-cost instrumentation will enable surgeons to easily validate implant alignment in real time,thereby reducing cost,operating room time,and future revision burden.