We present a novel approach for relocalization or place recognition, a fundamental problem to be solved in many robotics, automation, and AR applications. Rather than relying on often unstable appearance information, we consider a situation in which the reference map is given in the form of localized objects. Our localization framework relies on 3D semantic object detections, which are then associated to objects in the map. Possible pair-wise association sets are grown based on hierarchical clustering using a merge metric that evaluates spatial compatibility. The latter notably uses information about relative object configurations, which is invariant with respect to global transformations. Association sets are furthermore updated and expanded as the camera incrementally explores the environment and detects further objects. We test our algorithm in several challenging situations including dynamic scenes, large view-point changes, and scenes with repeated instances. Our experiments demonstrate that our approach outperforms prior art in terms of both robustness and accuracy.

翻译：我们提出了一个重新定位或地点识别的新办法,这是许多机器人、自动化和AR应用中需要解决的一个根本问题。我们不依赖经常不稳定的外观信息,而是考虑参考地图以本地物体的形式提供的情况。我们的本地化框架依靠3D语义对象探测,这些探测与地图中的对象相关。可能的双向关联组基于等级组合,使用一种评价空间兼容性的合并指标。后者主要使用相对物体配置的信息,而相对物体配置在全球变异方面是无法改变的。随着相机逐步探索环境并探测进一步的物体,协会各组得到进一步的更新和扩大。我们在几种具有挑战性的情况中测试我们的算法,包括动态场景、大视图点变化以及反复出现的场景。我们的实验表明,我们的方法在稳健性和准确性方面都超越了先前的艺术。