It depends on each dataset. But as far as I know, ground truths for this kind of problem are collected by high-end IMUs (NovAtel, OXTS, ...). The ready-to-use results of these sensors are put out after some algorithm to reduce noise and be considered with The Earth's magnetic field, ... However, I don't know, metrically, how those results are proved to be correct and how reliable they are. Anyone knows? 😅
Even the highest-end IMUs will drift and accumulate. In a small in-door case, one of the method I can conceive is to add a QR code on the robot, and use a fixed camera to capture its position. In outdoor case, perhaps use a high-precision GPS-RTK. The hardest case is large-scale in-door case.@216
Could be that it was to new for the Paper to be included? And i am not sure if DSO deserves the definition "Visual-Inertial" as i don't know what defines a VO as a VIO
Odometry is using a (any) sensor to determine how much distance has been traversed, so visual odometry (VO) is just clarification in which particular sensor for odometry is vision (a camera, typically).
citing the complete paper "We do not consider non-inertial visual simultaneous localization and mapping (SLAM) systems, for example ORBSLAM and LSD-SLAM. While these methods could potentially also be used for flying robot state estimation, we focus this benchmark on visual-inertial methods."
Nice overview - thanks for sharing!
Impressive work! When will the MSCKF_mono be public?
Howto get Ground truth of trajectory?
It depends on each dataset. But as far as I know, ground truths for this kind of problem are collected by high-end IMUs (NovAtel, OXTS, ...). The ready-to-use results of these sensors are put out after some algorithm to reduce noise and be considered with The Earth's magnetic field, ... However, I don't know, metrically, how those results are proved to be correct and how reliable they are. Anyone knows? 😅
Even the highest-end IMUs will drift and accumulate. In a small in-door case, one of the method I can conceive is to add a QR code on the robot, and use a fixed camera to capture its position. In outdoor case, perhaps use a high-precision GPS-RTK. The hardest case is large-scale in-door case.@216
hmm. How do you think OpenVINS, a fork of VINS-Mono, would compare to these other VIO systems?
thx for the nice sharing ! vins is the best algor!
What about ORB SLAM or DSO?
Could be that it was to new for the Paper to be included? And i am not sure if DSO deserves the definition "Visual-Inertial" as i don't know what defines a VO as a VIO
Odometry is using a (any) sensor to determine how much distance has been traversed, so visual odometry (VO) is just clarification in which particular sensor for odometry is vision (a camera, typically).
citing the complete paper "We do not consider non-inertial visual simultaneous localization and mapping (SLAM) systems, for example ORBSLAM and LSD-SLAM. While these methods
could potentially also be used for flying robot state estimation, we focus this benchmark on visual-inertial methods."
great, it's very helpful thank you