We present a system for Monocular Simultaneous Localization and Mapping (Mono-SLAM) relying solely on video input. Our algorithm makes it possible to precisely estimate the camera trajectory without relying on any motion model. The estimation is fully incremental: at a given time frame, only the current location is estimated while the previous camera positions are never modified. In particular, we do not perform any simultaneous iterative optimization of the camera positions and estimated 3D structure (local bundle adjustment).The key aspects of the system is a fast and simple pose estimation algorithm that uses information not only from the estimated 3D map, but also from the epipolar constraint. We show that the latter leads to a much more stable estimation of the camera trajectory than the conventional approach. We perform high precision camera trajectory estimation in urban scenes with a large amount of clutter. Using an omnidirectional camera placed on a vehicle, we cover the longest distance ever reported, up to 2.5 kilometers.
Abstract-We combine a visual odometry system with an aided inertial navigation filter to produce a precise and robust navigation system that does not rely on external infrastructure. Incremental structure from motion with sparse bundle adjustment using a stereo camera provides real-time highly accurate pose estimates of the sensor which are combined with six degree-of-freedom inertial measurements in an Extended Kalman Filter. The filter is structured to neatly handle the incremental and local nature of the visual odometry measurements and to handle uncertainties in the system in a principled manner. We present accurate results from data acquired in rural and urban scenes on a tractor and a passenger car travelling distances of several kilometers.
We have developed the CHIMP (CMU Highly Intelligent Mobile Platform) robot as a platform for executing complex tasks in dangerous, degraded, human‐engineered environments. CHIMP has a near‐human form factor, work‐envelope, strength, and dexterity to work effectively in these environments. It avoids the need for complex control by maintaining static rather than dynamic stability. Utilizing various sensors embedded in the robot's head, CHIMP generates full three‐dimensional representations of its environment and transmits these models to a human operator to achieve latency‐free situational awareness. This awareness is used to visualize the robot within its environment and preview candidate free‐space motions. Operators using CHIMP are able to select between task, workspace, and joint space control modes to trade between speed and generality. Thus, they are able to perform remote tasks quickly, confidently, and reliably, due to the overall design of the robot and software. CHIMP's hardware was designed, built, and tested over 15 months leading up to the DARPA Robotics Challenge. The software was developed in parallel using surrogate hardware and simulation tools. Over a six‐week span prior to the DRC Trials, the software was ported to the robot, the system was debugged, and the tasks were practiced continuously. Given the aggressive schedule leading to the DRC Trials, development of CHIMP focused primarily on manipulation tasks. Nonetheless, our team finished 3rd out of 16. With an upcoming year to develop new software for CHIMP, we look forward to improving the robot's capability and increasing its speed to compete in the DRC Finals.
Abstract. We present a new approach for self-calibrating the distortion function and the distortion center of cameras with general radially symmetric distortion. In contrast to most current models, we propose a model encompassing fisheye lenses as well as catadioptric cameras with a view angle larger than 180 o . Rather than representing distortion as an image displacement, we model it as a varying focal length, which is a function of the distance to the distortion center. This function can be discretized, acting as a general model, or represented with e.g. a polynomial expression. We present two flexible approaches for calibrating the distortion function. The first one is a plumbline-type method; images of line patterns are used to formulate linear constraints on the distortion function parameters. This linear system can be solved up to an unknown scale factor (a global focal length), which is sufficient for image rectification. The second approach is based on the first one and performs self-calibration from images of a textured planar object of unknown structure. We also show that by restricting the camera motion, self-calibration is possible from images of a completely unknown, non-planar scene. The analysis of rectified images, obtained using the computed distortion functions, shows very good results compared to other approaches and models, even those relying on non-linear optimization.
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