Projects per year
In this work, we present a real-time robust edge-based visual odometry framework for RGBD sensors (REVO). Even though our method is independent of the edge detection algorithm, we show that the use of state-of-the-art machine-learned edges gives significant improvements in terms of robustness and accuracy compared to standard edge detection methods. In contrast to approaches that heavily rely on the photo-consistency assumption, edges are less influenced by lighting changes and the sparse edge representation offers a larger convergence basin while the pose estimates are also very fast to compute. Further, we introduce a measure for tracking quality, which we use to determine when to insert a new key frame. We show the feasibility of our system on real-world datasets and extensively evaluate on standard benchmark sequences to demonstrate the performance in a wide variety of scenes and camera motions. Our framework runs in real-time on the CPU of a laptop computer and is available online.
|Title of host publication||Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS)|
|Publisher||Institute of Electrical and Electronics Engineers|
|Number of pages||8|
|Publication status||Published - 2017|
|Event||International Conference on Intelligent Robots and Systems 2017 - Vancouver, Canada|
Duration: 24 Sept 2017 → 28 Sept 2017
|Conference||International Conference on Intelligent Robots and Systems 2017|
|Period||24/09/17 → 28/09/17|
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- 1 Finished
CSISmartScan - Integrated 3D scene and documentation
1/10/15 → 30/09/17
Project: Research project