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Visual Simultaneous Localization and Mapping (VSLAM) systems are widely used in mobile robots for autonomous navigation. One important part of VSLAM is trajectory estimation. Trajectory estimation is a part of the localization task in VSLAM where a robot needs to estimate the camera pose in order to precisely align the real visited image locations. The poses are estimated using Visual Odometer Trajectory Estimation (VOTE) by extracting distinctive and traceable key points from sequence image locations having been visited by a robot. In the visual trajectory estimation, one of the most popular solutions is arguably PnP-RANSCA function. PnP-RANSAC is a common approach used for estimating the VOTE, which uses a feature descriptor such as SURF to extract key-points and match them in pairs based on their descriptors. However, due to the sensor noise and the high fluctuating scenes constitute an inevitable shortcoming that reduces the single visual descriptor performance in extracting the distinctive and traceable key points. Thus, this paper proposes a method that uses a random sampling scheme to combine the result of multiple key-points descriptors. The scheme extracts the best key points from SIFT, SURF and ORB key-point detectors based on their key-point response value. These key points are combined and refined based on Euclidean distances. This combination of key points with their corresponding visual descriptors is used in VOTE, which reduces the trajectory estimation errors. The proposed algorithm is evaluated on the widely used benchmark dataset KITTI where the three longest sequences are selected, 00 with 4541 …