Accurate depth estimation from a sequence of monocular images supported by proprioceptive sensors
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | 23rd Saint Petersburg International Conference on Integrated Navigation Systems, ICINS 2016 - Proceedings |
| Publisher | State Research Center of the Russian Federation |
| Pages | 249-257 |
| Number of pages | 9 |
| ISBN (Electronic) | 9785919950370 |
| Publication status | Published - 2016 |
| Publication type | A4 Article in a conference publication |
| Event | SAINT PETERSBURG INTERNATIONAL CONFERENCE ON INTEGRATED NAVIGATION SYSTEMS - Duration: 1 Jan 1900 → … |
Conference
| Conference | SAINT PETERSBURG INTERNATIONAL CONFERENCE ON INTEGRATED NAVIGATION SYSTEMS |
|---|---|
| Period | 1/01/00 → … |
Abstract
This paper describes an extended Kalman filter based algorithm for fusion of monocular vision measurements, inertial rate sensor measurements, and camera motion. The motion of the camera between successive images generates a baseline for range computations by triangulation. The recursive estimation algorithm is based on extended Kalman filtering. The depth estimation accuracy is strongly affected by mutual observer and feature point geometry, measurement accuracy of observer motion parameters and line of sight to a feature point. The simulation study investigates how the estimation accuracy is affected by the following parameters: linear and angular velocity measurement errors, camera noise, and observer path. These results draw requirements to the instrumentation and observation scenarios. It was found that under favorable conditions the error in distance estimation does not exceed 2% of the distance to a feature point.
ASJC Scopus subject areas
Keywords
- Computer vision, Gyroscope, IMU, Odometer, Structure from motion