The main challenge in articulated body motion tracking is the large number of degrees of freedom (around 30)

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This paper presents a method for Simultaneous Localization and Mapping (SLAM), relying on a monocular camera as the only sensor, which is able to build outdoor, closed-loop maps much larger than previously achieved with such input. Our system, based on the Hierarchical Map approach [1], builds independent local maps in real-time using the EKF-SLAM technique and the inverse depth representation proposed in [2]. The main novelty in the local mapping process is the use of a data association technique that greatly improves its robustness in dynamic and complex environments. A new visual map matching algorithm stitches these maps together and is able to detect large loops automatically, taking into account the unobservability of scale intrinsic to pure monocular SLAM. The loop closing constraint is applied at the upper level of the Hierarchical Map in near real-time.We present experimental results demonstrating monocular SLAM as a human carries a camera over long walked trajectories in outdoor areas with people and other clutter, even in the more difficult case of forward-looking camera, and show the closing of loops of several hundred meters.

Abstract. We derive a probabilistic framework for robust, real-time, visual tracking of previously unseen objects from a moving camera. The tracking problem is handled using a bag-of-pixels representation and comprises a rigid registration between frames, a segmentation and online appearance learning. The registration compensates for rigid motion, segmentation models any residual shape deformation and the online appearance learning provides continual refinement of both the object and background appearance models. The key to the success of our method is the use of pixel-wise posteriors, as opposed to likelihoods. We demonstrate the superior performance of our tracker by comparing cost function statistics against those commonly used in the visual tracking literature. Our comparison method provides a way of summarising tracking performance using lots of data from a variety of different sequences.

1Ecologists are increasingly using technology to improve the quality of data collected on wildlife, 2 particularly for assessing the environmental impacts of human activities. Remotely Piloted 3 Aircraft Systems (RPAS; commonly known as 'drones') are widely touted as a cost-effective 4 way to collect high quality wildlife population data, however, the validity of these claims is 5 unclear. Using life-sized seabird colonies containing a known number of replica birds, we show 6 that RPAS-derived data are, on average, between 43% and 96% more accurate than data from 7 the traditional ground-based collection method. We also demonstrate that counts from this 8 remotely sensed imagery can be semi-automated with a high degree of accuracy. The 9 increased accuracy and precision of RPAS-derived wildlife monitoring data provides greater 10 statistical power to detect fine-scale population fluctuations allowing for more informed and 11 proactive ecological management. 12

The use of line features in real-time visual tracking applications is commonplace when a prior map is available, but building the map while tracking in real-time is much more difficult. We describe how straight lines can be added to a monocular Extended Kalman Filter Simultaneous Mapping and Localisation (EKF SLAM) system in a manner that is both fast and which integrates easily with point features. To achieve real-time operation, we present a fast straight-line detector that hypothesises and tests straight lines connecting detected seed points. We demonstrate that the resulting system provides good camera localisation and mapping in real-time on a standard workstation, using either line features alone, or lines and points combined.

Abstract-It is well known that bundle adjustment is the optimal non-linear least-squares formulation of the simultaneous localization and mapping problem, in that its maximum likelihood form matches the definition of the Cramer Rao Lower Bound. Unfortunately, computing the ML solution is often prohibitively expensive -this is especially true during loop closures, which often necessitate adjusting all parameters in a loop. In this paper we note that it is precisely the choice of a single privileged coordinate frame that makes bundle adjustment costly, and that this expense can be avoided by adopting a completely relative approach. We derive a new relative bundle adjustment, which instead of optimizing in a single Euclidean space, works in a metric-space defined by a manifold. Using an adaptive optimization strategy, we show experimentally that it is possible to solve for the full ML solution incrementally in constant time -even at loop closure. Our system also operates online in real-time using stereo data, with fast appearance-based loop closure detection. We show results for sequences of 23k frames over 1.08km that indicate the accuracy of the approach.