Robust Stereo Visual Odometry Using Improved RANSAC-Based Methods for Mobile Robot Localization.In this paper, we present a novel approach for stereo visual odometry with robust motion estimation that is faster and more accurate than standard RANSAC (Random Sample Consensus).  Our method makes improvements in RANSAC in three aspects: first, the hypotheses are preferentially generated by sampling the input feature points on the order of ages and similarities of the features; second, the evaluation of hypotheses is performed based on the SPRT (Sequential Probability Ratio Test) that makes bad hypotheses discarded very fast without verifying all the data points; third, we aggregate the three best hypotheses to get the final estimation instead of only selecting the best hypothesis.  The first two aspects improve the speed of RANSAC by generating good hypotheses and discarding bad hypotheses in advance, respectively.  Experimental results show that the proposed method achieves better results for both speed and accuracy than RANSAC.
RANSAC
Robust Stereo Visual Odometry Using Improved RANSAC-Based Methods for Mobile Robot Localization.In this paper, we present a novel approach for stereo visual odometry with robust motion estimation that is faster and more accurate than standard RANSAC (Random Sample Consensus).  Our method makes improvements in RANSAC in three aspects: first, the hypotheses are preferentially generated by sampling the input feature points on the order of ages and similarities of the features; second, the evaluation of hypotheses is performed based on the SPRT (Sequential Probability Ratio Test) that makes bad hypotheses discarded very fast without verifying all the data points; third, we aggregate the three best hypotheses to get the final estimation instead of only selecting the best hypothesis.  The first two aspects improve the speed of RANSAC by generating good hypotheses and discarding bad hypotheses in advance, respectively.  Experimental results show that the proposed method achieves better results for both speed and accuracy than RANSAC.
RANSAC
Robust Stereo Visual Odometry Using Improved RANSAC-Based Methods for Mobile Robot Localization.In this paper, we present a novel approach for stereo visual odometry with robust motion estimation that is faster and more accurate than standard RANSAC (Random Sample Consensus).  Our method makes improvements in RANSAC in three aspects: first, the hypotheses are preferentially generated by sampling the input feature points on the order of ages and similarities of the features; second, the evaluation of hypotheses is performed based on the SPRT (Sequential Probability Ratio Test) that makes bad hypotheses discarded very fast without verifying all the data points; third, we aggregate the three best hypotheses to get the final estimation instead of only selecting the best hypothesis.  The first two aspects improve the speed of RANSAC by generating good hypotheses and discarding bad hypotheses in advance, respectively.  Experimental results show that the proposed method achieves better results for both speed and accuracy than RANSAC.
RANSAC
Our method makes improvements in RANSAC in three aspects: first, the hypotheses are preferentially generated by sampling the input feature points on the order of ages and similarities of the features; second, the evaluation of hypotheses is performed based on the SPRT (Sequential Probability Ratio Test) that makes bad hypotheses discarded very fast without verifying all the data points; third, we aggregate the three best hypotheses to get the final estimation instead of only selecting the best hypothesis. 
SPRT
Robust Stereo Visual Odometry Using Improved RANSAC-Based Methods for Mobile Robot Localization.In this paper, we present a novel approach for stereo visual odometry with robust motion estimation that is faster and more accurate than standard RANSAC (Random Sample Consensus).  Our method makes improvements in RANSAC in three aspects: first, the hypotheses are preferentially generated by sampling the input feature points on the order of ages and similarities of the features; second, the evaluation of hypotheses is performed based on the SPRT (Sequential Probability Ratio Test) that makes bad hypotheses discarded very fast without verifying all the data points; third, we aggregate the three best hypotheses to get the final estimation instead of only selecting the best hypothesis.  The first two aspects improve the speed of RANSAC by generating good hypotheses and discarding bad hypotheses in advance, respectively.  Experimental results show that the proposed method achieves better results for both speed and accuracy than RANSAC.
RANSAC