75 research outputs found
Accelerating Globally Optimal Consensus Maximization in Geometric Vision
Branch-and-bound-based consensus maximization stands out due to its important
ability of retrieving the globally optimal solution to outlier-affected
geometric problems. However, while the discovery of such solutions caries high
scientific value, its application in practical scenarios is often prohibited by
its computational complexity growing exponentially as a function of the
dimensionality of the problem at hand. In this work, we convey a novel, general
technique that allows us to branch over an dimensional space for an
n-dimensional problem. The remaining degree of freedom can be solved globally
optimally within each bound calculation by applying the efficient interval
stabbing technique. While each individual bound derivation is harder to compute
owing to the additional need for solving a sorting problem, the reduced number
of intervals and tighter bounds in practice lead to a significant reduction in
the overall number of required iterations. Besides an abstract introduction of
the approach, we present applications to three fundamental geometric computer
vision problems: camera resectioning, relative camera pose estimation, and
point set registration. Through our exhaustive tests, we demonstrate
significant speed-up factors at times exceeding two orders of magnitude,
thereby increasing the viability of globally optimal consensus maximizers in
online application scenarios
Intuitive 3D Maps for MAV Terrain Exploration and Obstacle Avoidance
Recent development showed that Micro Aerial Vehicles (MAVs) are nowadays capable of autonomously take off at one point and land at another using only one single camera as exteroceptive sensor. During the flight and landing phase the MAV and user have, however, little knowledge about the whole terrain and potential obstacles. In this paper we show a new solution for a real-time dense 3D terrain reconstruction. This can be used for efficient unmanned MAV terrain exploration and yields a solid base for standard autonomous obstacle avoidance algorithms and path planners. Our approach is based on a textured 3D mesh on sparse 3D point features of the scene. We use the same feature points to localize and control the vehicle in the 3D space as we do for building the 3D terrain reconstruction mesh. This enables us to reconstruct the terrain without significant additional cost and thus in real-time. Experiments show that the MAV is easily guided through an unknown, GPS denied environment. Obstacles are recognized in the iteratively built 3D terrain reconstruction and are thus well avoide
Scale jump-aware pose graph relaxation for monocular SLAM with re-initializations
Pose graph relaxation has become an indispensable addition to SLAM enabling
efficient global registration of sensor reference frames under the objective of
satisfying pair-wise relative transformation constraints. The latter may be
given by incremental motion estimation or global place recognition. While the
latter case enables loop closures and drift compensation, care has to be taken
in the monocular case in which local estimates of structure and displacements
can differ from reality not just in terms of noise, but also in terms of a
scale factor. Owing to the accumulation of scale propagation errors, this scale
factor is drifting over time, hence scale-drift aware pose graph relaxation has
been introduced. We extend this idea to cases in which the relative scale
between subsequent sensor frames is unknown, a situation that can easily occur
if monocular SLAM enters re-initialization and no reliable overlap between
successive local maps can be identified. The approach is realized by a hybrid
pose graph formulation that combines the regular similarity consistency terms
with novel, scale-blind constraints. We apply the technique to the practically
relevant case of small indoor service robots capable of effectuating purely
rotational displacements, a condition that can easily cause tracking failures.
We demonstrate that globally consistent trajectories can be recovered even if
multiple re-initializations occur along the loop, and present an in-depth study
of success and failure cases.Comment: 8 pages, 23 figures, International Conference on Intelligent Robots
and Systems 202
The generalized relative pose and scale problem: View-graph fusion via 2D-2D registration
It is well-known that the relative pose problem can be
generalized to non-central cameras. We present a further
generalization, denoted the generalized relative pose
and scale problem. It has surprising importance for classical
problems such as solving similarity transformations
for view-graph concatenation in hierarchical structure from
motion and loop-closure in visual SLAM, both posed as a
2D-2D registration problem. The relative pose problem and
all its generalizations constitute a family of similar symmetric
eigenvalue problems, which allow us to compress data
and find a geometrically meaningful solution by an efficient
search in the space of rotations. While the derivation of a
completely general closed-form solver appears intractable,
we make use of a simple heuristic global energy minimization
scheme based on local minimum suppression, returning
outstanding performance in practically relevant scenarios.
Efficiency and reliability of our algorithm are demonstrated
on both simulated and real data, supporting our claim of superior
performance with respect to both generalized 2D-3D
and 3D-3D registration approaches. By directly employing
image information, we avoid the common noise in point
clouds occuring especially along the depth direction.This research is supported by the ARC Centre of Excellence for Robotic Vision, as well as the ARC grant
DE150101365. The work is also supported by NSF Grant
IIS-1219261, ONR Grant N00014-14-1- 0133 and NSF
Graduate Research Fellowship Grant DGE- 1144085
Semi-Dense Visual Odometry for RGB-D Cameras Using Approximate Nearest Neighbour Fields
This paper presents a robust and efficient semidense visual odometry solution for RGB-D cameras. The core of our method is a 2D-3D ICP pipeline which estimates the pose of the sensor by registering the projection of a 3D semidense map of a reference frame with the 2D semi-dense region extracted in the current frame. The processing is speeded up by efficiently implemented approximate nearest neighbour fields under the Euclidean distance criterion, which permits the use of compact Gauss-Newton updates in the optimization. The registration is formulated as a maximum a posterior problem to deal with outliers and sensor noise, and the equivalent weighted least squares problem is consequently solved by iteratively reweighted least squares method. A variety of robust weight functions are tested and the optimum is determined based on the probabilistic characteristics of the sensor model. Extensive evaluation on publicly available RGB-D datasets shows that the proposed method predominantly outperforms existing state-of-the-art methods.The work is furthermore
supported by ARC grants DE150101365. Yi Zhou acknowledges the financial support from the China Scholarship Council for his PhD Scholarship
No.20140602009
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