Abstract-In this paper, we describe a system that can carry out simultaneous localization and mapping (SLAM) in large indoor and outdoor environments using a stereo pair moving with 6 DOF as the only sensor. Unlike current visual SLAM systems that use either bearing-only monocular information or 3-D stereo information, our system accommodates both monocular and stereo. Textured point features are extracted from the images and stored as 3-D points if seen in both images with sufficient disparity, or stored as inverse depth points otherwise. This allows the system to map both near and far features: the first provide distance and orientation, and the second provide orientation information. Unlike other vision-only SLAM systems, stereo does not suffer from "scale drift" because of unobservability problems, and thus, no other information such as gyroscopes or accelerometers is required in our system. Our SLAM algorithm generates sequences of conditionally independent local maps that can share information related to the camera motion and common features being tracked. The system computes the full map using the novel conditionally independent divide and conquer algorithm, which allows constant time operation most of the time, with linear time updates to compute the full map. To demonstrate the robustness and scalability of our This paper has supplementary downloadable multimedia material available at http://ieeexplore.ieee.org provided by the author. This material includes the following video files. VSLAM_stereo_distribution.avi: Suppose that a point is located in front of a stereo camera at different distances ranging from 1 to 20 m. The video shows the uncertainty distributions estimated by different methods when the point position is reconstructed from a noisy stereo pair observation. Cloud points are samples obtained from the real distribution, red ellipses represent uncertainties estimated using depth point parameterization, and the interior of the green regions represent the uncertainties estimated using inverse depth parameterization. It is clear from the video that the depth point parameterization is overconfident when the original point is far from the stereo. VSLAM_local_map.avi: In this video, the process of building a local map is shown. The video reproduces the sequence of images captured by the stereo as well as a top and lateral perspectives of the local map built.
In REF , it points out that the monocular camera suffers from the "scale drift" problem; however, the stereo camera can efficiently overcome this issue.
Large-Scale 6-DOF SLAM With Stereo-in-Hand
IEEE Transactions on Robotics
IEEE Transactions on Robotics
Mathematics Computer Science