In this paper, we present a new 3D tracking approach for self-localization in urban environments. In particular, we build on existing tracking approaches (i.e., visual odometry tracking and SLAM), additionally using the information provided by 2.5D maps of the environment. Since this combination is not straightforward, we adopt ideas from semantic segmentation to find a better alignment between the pose estimated by the tracker and the 2.5D model. Specifically, we show that introducing edges as semantic classes is highly beneficial for our task. In this way, we can reduce tracker inaccuracies and prevent drifting, thus increasing the tracker’s stability. We evaluate our approach for two different challenging scenarios, also showing that it is generally applicable in different application domains and that we are not limited to a specific tracking method.
|Title of host publication||Proceedings of the British Machine Vision Conference (BMVC)|
|Publication status||Published - 2017|
|Event||British Machine Vision Conference 2017 - London, United Kingdom|
Duration: 4 Sep 2017 → 7 Sep 2017
|Conference||British Machine Vision Conference 2017|
|Abbreviated title||BMVC 2017|
|Period||4/09/17 → 7/09/17|