252 research outputs found
LDSO: Direct Sparse Odometry with Loop Closure
In this paper we present an extension of Direct Sparse Odometry (DSO) to a
monocular visual SLAM system with loop closure detection and pose-graph
optimization (LDSO). As a direct technique, DSO can utilize any image pixel
with sufficient intensity gradient, which makes it robust even in featureless
areas. LDSO retains this robustness, while at the same time ensuring
repeatability of some of these points by favoring corner features in the
tracking frontend. This repeatability allows to reliably detect loop closure
candidates with a conventional feature-based bag-of-words (BoW) approach. Loop
closure candidates are verified geometrically and Sim(3) relative pose
constraints are estimated by jointly minimizing 2D and 3D geometric error
terms. These constraints are fused with a co-visibility graph of relative poses
extracted from DSO's sliding window optimization. Our evaluation on publicly
available datasets demonstrates that the modified point selection strategy
retains the tracking accuracy and robustness, and the integrated pose-graph
optimization significantly reduces the accumulated rotation-, translation- and
scale-drift, resulting in an overall performance comparable to state-of-the-art
feature-based systems, even without global bundle adjustment
Monocular Visual Odometry for Fixed-Wing Small Unmanned Aircraft Systems
The popularity of small unmanned aircraft systems (SUAS) has exploded in recent years and seen increasing use in both commercial and military sectors. A key interest area for the military is to develop autonomous capabilities for these systems, of which navigation is a fundamental problem. Current navigation solutions suffer from a heavy reliance on a Global Positioning System (GPS). This dependency presents a significant limitation for military applications since many operations are conducted in environments where GPS signals are degraded or actively denied. Therefore, alternative navigation solutions without GPS must be developed and visual methods are one of the most promising approaches. A current visual navigation limitation is that much of the research has focused on developing and applying these algorithms on ground-based vehicles, small hand-held devices or multi-rotor SUAS. However, the Air Force has a need for fixed-wing SUAS to conduct extended operations. This research evaluates current state-of-the-art, open-source monocular visual odometry (VO) algorithms applied on fixed-wing SUAS flying at high altitudes under fast translation and rotation speeds. The algorithms tested are Semi-Direct VO (SVO), Direct Sparse Odometry (DSO), and ORB-SLAM2 (with loop closures disabled). Each algorithm is evaluated on a fixed-wing SUAS in simulation and real-world flight tests over Camp Atterbury, Indiana. Through these tests, ORB-SLAM2 is found to be the most robust and flexible algorithm under a variety of test conditions. However, all algorithms experience great difficulty maintaining localization in the collected real-world datasets, showing the limitations of using visual methods as the sole solution. Further study and development is required to fuse VO products with additional measurements to form a complete autonomous navigation solution
Loosely-Coupled Semi-Direct Monocular SLAM
We propose a novel semi-direct approach for monocular simultaneous
localization and mapping (SLAM) that combines the complementary strengths of
direct and feature-based methods. The proposed pipeline loosely couples direct
odometry and feature-based SLAM to perform three levels of parallel
optimizations: (1) photometric bundle adjustment (BA) that jointly optimizes
the local structure and motion, (2) geometric BA that refines keyframe poses
and associated feature map points, and (3) pose graph optimization to achieve
global map consistency in the presence of loop closures. This is achieved in
real-time by limiting the feature-based operations to marginalized keyframes
from the direct odometry module. Exhaustive evaluation on two benchmark
datasets demonstrates that our system outperforms the state-of-the-art
monocular odometry and SLAM systems in terms of overall accuracy and
robustness.Comment: Accepted for publication in IEEE Robotics and Automation Letters.
Watch video demo at: https://youtu.be/j7WnU7ZpZ8
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