DRAFT SUBMISSION - PLEASE DO NOT REDISTRIBUTE Motion Estimation from Image and Inertial Measurements (revised 2/29/04)

Cameras and inertial sensors are each good can- didates for autonomous vehicle navigation, modeling from video, and other applications that require six de- gree of freedom motion estimation. But, these sensors are also good candidates to be deployed together, since each can be used to resolve the ambiguities in esti- mated motion that result from using the other modality alone. In this paper, we consider the specific problem of estimating sensor motion and other unknowns from image, gyro, and accelerometer measurements, in en- vironments without known fiducials. This work targets applications where external positions references such as global positioning are not available, and focuses on the use of small and inexpensive inertial sensors, for applications where weight and cost requirements pre- clude the use of precision inertial navigation systems. We present two algorithms for estimating sensor motion from image and inertial measurements. The first algorithm is a batch method, which produces esti- mates of the sensor motion, scene structure, and other unknowns using measurements from the entire obser- vation sequence simultaneously. The second algorithm recovers sensor motion, scene structure, and other pa- rameters recursively, and is suitable for use with long or "infinite" sequences, in which no feature is always visible. We evaluate the accuracy of the algorithms and their sensitivity to their estimation parameters using a sequence of four experiments. These experiments focus on cases where estimates from image or iner- tial measurements alone are poor, on the relative ad- vantage of using inertial measurements and omnidi- rectional images, and on long sequences in which the percentage of the image sequence in which individual features are visible is low.