Loop detection plays a key role in visual Simultaneous Localization and
Mapping (SLAM) by correcting the accumulated pose drift. In indoor scenarios,
the richly distributed semantic landmarks are view-point invariant and hold
strong descriptive power in loop detection. The current semantic-aided loop
detection embeds the topology between semantic instances to search a loop.
However, current semantic-aided loop detection methods face challenges in
dealing with ambiguous semantic instances and drastic viewpoint differences,
which are not fully addressed in the literature. This paper introduces a novel
loop detection method based on an incrementally created scene graph, targeting
the visual SLAM at indoor scenes. It jointly considers the macro-view topology,
micro-view topology, and occupancy of semantic instances to find correct
correspondences. Experiments using handheld RGB-D sequence show our method is
able to accurately detect loops in drastically changed viewpoints. It maintains
a high precision in observing objects with similar topology and appearance. Our
method also demonstrates that it is robust in changed indoor scenes.

介绍了一种基于逐步创建的场景图的新型回路检测方法，针对室内场景的视觉 SLAM。它同时考虑了宏观视图拓扑、微观视图拓扑和语义实例的占用率来找到正确的对应关系。

基于场景图的视角不变和准确的循环检测方法

Towards View-invariant and Accurate Loop Detection Based on Scene Graph

Multi-beam LiDAR sensors are increasingly used in robotics, particularly with
autonomous cars for localization and perception tasks, both relying on the
ability to build a precise map of the environment. For this, we propose a new
real-time LiDAR-only odometry method called CT-ICP (for Continuous-Time ICP),
completed into a full SLAM with a novel loop detection procedure. The core of
this method, is the introduction of the combined continuity in the scan
matching, and discontinuity between scans. It allows both the elastic
distortion of the scan during the registration for increased precision, and the
increased robustness to high frequency motions from the discontinuity.
We build a complete SLAM on top of this odometry, using a fast pure LiDAR
loop detection based on elevation image 2D matching, providing a pose graph
with loop constraints. To show the robustness of the method, we tested it on
seven datasets: KITTI, KITTI-raw, KITTI-360, KITTI-CARLA, ParisLuco, Newer
College, and NCLT in driving and high-frequency motion scenarios. Both the
CT-ICP odometry and the loop detection are made available online. CT-ICP is
currently first, among those giving access to a public code, on the KITTI
odometry leaderboard, with an average Relative Translation Error (RTE) of 0.59%
and an average time per scan of 60ms on a CPU with a single thread.

本研究提出了一种新的实时 LiDAR-only 里程计方法（CT-ICP）和一种新的环路检测程序，结合弹性形变和高频运动鲁棒性，使用 2D 匹配进行纯激光雷达回环检测和构建完整的 SLAM，应用于自动驾驶汽车的定位和感知任务。测试结果表明，相对位移误差（RTE）平均值为 0.59％，每次扫描的平均时间为 60 毫秒，是 KITTI 里程计排行榜上代码公开最优秀的之一。