Dynamic particle filter parameterization

1. A method, comprising:

estimating a rotation of an apparatus; and

estimating a position variation of a motion of the apparatus between a first instant and a second instant, said motion including the rotation of the apparatus and said position variation, said position variation including a position and a velocity, wherein estimating said position variation comprises performing particle filtering to estimate said position variation from a probabilistic-weighted average of particles, said particle filtering having as inputs the estimated rotation and a parameter indicative of a quality of the estimated rotation, the parameter being based on an inliers rate of the estimated rotation.

2. The method according to claim 1 , wherein said parameter is used in estimating the velocity of the position variation.

3. The method according to claim 2 , wherein estimating said velocity comprises guiding particle spreading with a pedestrian step detection technique using accelerometer data.

4. The method according to claim 3 , wherein the velocity estimation takes into account said parameter and a random 3D vector generated according to a Gaussian distribution involving an expected velocity for the second image, and said expected velocity for the second image is calculated by taking into account a current estimated rotation matrix, a velocity of step and a frame rate of the apparatus.

5. The method according to claim 1 , wherein said apparatus is intended to capture a sequence of images, a first image being captured at said first instant, a second image being captured at said second instant, said motion is an ego-motion of the apparatus between the first image and the second image including a 3D rotation of the apparatus and a position variation of the apparatus in the 3D space, and each particle is a virtual apparatus.

6. The method according to claim 5 , wherein said 3D rotation is estimated by performing a RANSAC type algorithm providing the inliers rate.

7. The method according to claim 6 , wherein said parameter is a relationship between said inliers rate, a reprojection error of a best particle, and a targeted reprojection error.

8. The method of claim 1 , comprising:

generating a mapping of an environment of the apparatus based on the estimated position variation.

9. A device, comprising:

means for estimating a rotation of an apparatus; and

means for estimating a position variation of a motion of the apparatus between a first instant and a second instant, said motion including the rotation of the apparatus and said position variation, said position variation including a position and a velocity, wherein said means for estimating the position variation includes means for particle filtering of particles for estimating said position and velocity from a probabilistic-weighted average of the particles, said particle filtering having as inputs the estimated rotation and a parameter indicative of a quality of said rotation estimation, the parameter being based on an inliers rate of the estimated rotation.

10. The device according to claim 9 , wherein said parameter is used to estimate the velocity of the position variation.

11. The device according to claim 10 , wherein said estimating said velocity includes guiding the particle spreading with a pedestrian step detection technique using accelerometer data.

12. The device according to claim 11 , wherein said estimating the velocity takes into account said parameter and a random 3D vector generated according to a Gaussian distribution involving an expected velocity for the second image, and said expected velocity for the second image takes into account a current estimated rotation matrix, a velocity of step and a frame rate of the apparatus.

13. The device according to claim 9 , wherein said apparatus is configured to capture a sequence of images, including a first image captured at said first instant, and a second image captured at said second instant, said motion is an ego-motion of the apparatus between the first image and the second image including a 3D rotation of the apparatus and a position variation of the apparatus in the 3D space, and each particle is a virtual apparatus.

14. The device according to claim 13 , wherein the means for estimating rotation estimates said 3D rotation by performing a RANSAC type algorithm providing the inliers rate.

15. The device according to claim 14 , wherein said parameter is a relationship between said inliers rate, a reprojection error of a best particle, and targeted reprojection error.

16. The device of claim 9 , comprising:

means for generating a mapping of an environment of the apparatus based on the estimated position variation.

17. A platform, comprising

an apparatus; and

a device including means for estimating a rotation of the apparatus and means for estimating a position variation of a motion of the apparatus between a first instant and a second instant, said motion including the rotation of the apparatus and said position variation, said position variation including a position and a velocity, wherein said means for estimating the position variation includes a particle filter for particle filtering of particles for estimating said position and velocity from a probabilistic-weighted average of the particles, said particle filtering having as inputs the estimated rotation and a parameter indicative of a quality of said rotation estimation, the parameter being based on an inliers rate of the estimated rotation.

18. The platform according to claim 17 , wherein said apparatus is configured to capture a sequence of images, including a first image captured at said first instant, and a second image captured at said second instant, said motion is an ego-motion of the apparatus between the first image and the second image including a 3D rotation of the apparatus and a position variation of the apparatus in the 3D space, and each particle is a virtual apparatus.

19. The platform according to claim 18 , wherein the estimating the rotation of the apparatus includes estimating a rotation in said 3D space by performing a RANSAC type algorithm providing the inliers rate.

20. The platform according to claim 17 , wherein said estimating said velocity comprising guiding the particle spreading with a pedestrian step detection technique using accelerometer data.

21. The platform according to claim 20 wherein the velocity estimation takes into account said parameter and a random 3D vector generated according to a Gaussian distribution involving an expected velocity for the second image, and said expected velocity for the second image takes into account a current estimated rotation matrix, a velocity of step and a frame rate of the apparatus.

22. The platform of claim 17 , comprising:

means for generating a mapping of an environment of the apparatus based on the estimated position variation.

23. A system, comprising:

one or more sensors; and

processing circuitry coupled to the one or more sensors, and which, in operation:

estimates a rotation of an apparatus; and

estimates a position variation of a motion of the apparatus between a first instant and a second instant, the motion including the rotation of the apparatus and the position variation, the position variation including a position and a velocity, wherein said position variation is estimated using particle filtering to estimate said position variation from a probabilistic-weighted average of particles, said particle filtering having as inputs the estimated rotation and a parameter indicative of a quality of the estimated rotation, the parameter being based on an inliers rate of the estimated rotation.

24. The system of claim 23 wherein said parameter is used in estimating the velocity of the position variation.

25. The system of claim 23 wherein the one or more sensor comprise an image sensor which, in operation, captures a sequence of images, a first image being captured at said first instant, a second image being captured at said second instant, said motion is an ego-motion of the apparatus between the first image and the second image including a 3D rotation of the apparatus and a position variation of the apparatus in the 3D space, and each particle is a virtual apparatus.

26. The system of claim 25 wherein said 3D rotation is estimated by performing a RANSAC type algorithm providing the inliers rate.

27. The system of claim 25 wherein the one or more sensors comprise an accelerometer.

28. The system of claim 23 wherein the processing circuitry, in operation, generates a mapping of an environment of the apparatus based on the estimated position variation.