Camera motion estimation through planar deformation determination

In this paper, we propose a global method for estimating the motion of a camera which films a static scene. Our approach is direct, fast and robust, and deals with adjacent frames of a sequence. It is based on a quadratic approximation of the deformation between two images, in the case of a scene with constant depth in the camera coordinate system. This condition is very restrictive but we show that provided translation and depth inverse variations are small enough, the error on optical flow involved by the approximation of depths by a constant is small. In this context, we propose a new model of camera motion, that allows to separate the image deformation in a similarity and a ``purely'' projective application, due to change of optical axis direction. This model leads to a quadratic approximation of image deformation that we estimate with an M-estimator; we can immediatly deduce camera motion parameters.Comment: 21 pages, version modifi\'ee accept\'e le 20 mars 200

EKF-Based Recursive Dual Estimation of Structure&Motion from Stereo Data

Extended Kalman filters (EKF) have been proposed to estimate ego-motion and to recursively update scene structure in the form of 3-D positions of selected prominent features from motion and stereo sequences. Previous methods typically accommodate no more than a few dozen features for real-time processing. To maintain motion estimation accuracy, this calls for high contrast images to compute image feature locations with precision. Within manmade environments, various prominent corner points exist that can be extracted and tracked with required accuracy. However, prominent features are more difficult to localize precisely in natural scenes. Statistically, more feature points become necessary to maintain the same level of motion estimation accuracy and robustness. However, this imposes a computational burden beyond the capability of EKF-based techniques for real-time processing. A sequential dual EKF estimator utilizing stereo data is proposed for improved computation efficiency. Two important issues, unbiased estimation and stochastic stability are addressed. Furthermore, the dynamic feature set is handled in a more effective, efficient and robust way. Experimental results to demonstrate the merits of the new theoretical and algorithmic developments are presented

Positioning and Photo-Mosaicking with Long Image Sequences; Comparison of Selected Methods

Despite limited range, high resolution and data rate are among factors motivating the investigation of vision-based technologies in support of unmanned submersible platform operations. Among many, automatic vision-guided station keeping, localization and navigation, photo-mosaicking and 3-D mapping comprise application areas of special interest. The core issue in realizing these capabilities is to know with high accuracy the motion and (or) position of the vehicle

Underwater Forward-Scan Sonar Video Coding by Background Modeling and Synthesis for Real-Time Transmission

The traditional navigation cameras are being replaced by 2-D forward-scan sonar video devices, when untethered submersible robots operate in underwater environments with poor visibility. A typical FS sonar image comprises of object highlight and cast shadow regions, as well as large relatively uniform background areas, generally corrupted with high level of speckle noise. While both are important in image analysis for object and scene interpretation, highlight and shadow regions encode valuable information for inferring 3-D shape. For some tasks, real-time man-machine interaction and cooperation can significantly enhance the robot performance. The transmission of live video from the robotic platform to a remote surface station requires high video compression ratios to meet the current low-bandwidth limitations of acoustic channels. This work makes use of a novel forward-scan sonar image coding scheme, where 1) object highlights and shadow edges (treated as foreground) are segmented from the less informative background, maintaining high details for encoding; 2) background is highly compressed to transmit at very little cost; 3) together, they are decoded at the receiver end for reconstruction. Application to various video sequences shows an average compression ratio of 100, for raw video data at 512×96 resolution transmitted at 10 frame per second