Monocular visual simultaneous localization and mapping (V-SLAM) is nowadays
an irreplaceable tool in mobile robotics and augmented reality, where it
performs robustly. However, human colonoscopies pose formidable challenges like
occlusions, blur, light changes, lack of texture, deformation, water jets or
tool interaction, which result in very frequent tracking losses. ORB-SLAM3, the
top performing multiple-map V-SLAM, is unable to recover from them by merging
sub-maps or relocalizing the camera, due to the poor performance of its place
recognition algorithm based on ORB features and DBoW2 bag-of-words.
We present CudaSIFT-SLAM, the first V-SLAM system able to process complete
human colonoscopies in real-time. To overcome the limitations of ORB-SLAM3, we
use SIFT instead of ORB features and replace the DBoW2 direct index with the
more computationally demanding brute-force matching, being able to successfully
match images separated in time for relocation and map merging. Real-time
performance is achieved thanks to CudaSIFT, a GPU implementation for SIFT
extraction and brute-force matching.
We benchmark our system in the C3VD phantom colon dataset, and in a full real
colonoscopy from the Endomapper dataset, demonstrating the capabilities to
merge sub-maps and relocate in them, obtaining significantly longer sub-maps.
Our system successfully maps in real-time 88 % of the frames in the C3VD
dataset. In a real screening colonoscopy, despite the much higher prevalence of
occluded and blurred frames, the mapping coverage is 53 % in carefully explored
areas and 38 % in the full sequence, a 70 % improvement over ORB-SLAM3.

CudaSIFT-SLAM 是首个能够实时处理完整人类结肠镜检测的 V-SLAM 系统，通过使用 SIFT 特征替代 ORB 特征以及更计算密集的暴力匹配算法，成功地解决了 ORB-SLAM3 的限制，并能够在时间上匹配图像以便重定位和地图融合。

CudaSIFT-SLAM：针对真人内窥镜手术全过程建图的多地图视觉 SLAM

CudaSIFT-SLAM: multiple-map visual SLAM for full procedure mapping in  real human endoscopy

Object goal navigation is an important problem in Embodied AI that involves
guiding the agent to navigate to an instance of the object category in an
unknown environment -- typically an indoor scene. Unfortunately, current
state-of-the-art methods for this problem rely heavily on data-driven
approaches, \eg, end-to-end reinforcement learning, imitation learning, and
others. Moreover, such methods are typically costly to train and difficult to
debug, leading to a lack of transferability and explainability. Inspired by
recent successes in combining classical and learning methods, we present a
modular and training-free solution, which embraces more classic approaches, to
tackle the object goal navigation problem. Our method builds a structured scene
representation based on the classic visual simultaneous localization and
mapping (V-SLAM) framework. We then inject semantics into geometric-based
frontier exploration to reason about promising areas to search for a goal
object. Our structured scene representation comprises a 2D occupancy map,
semantic point cloud, and spatial scene graph.
Our method propagates semantics on the scene graphs based on language priors
and scene statistics to introduce semantic knowledge to the geometric
frontiers. With injected semantic priors, the agent can reason about the most
promising frontier to explore. The proposed pipeline shows strong experimental
performance for object goal navigation on the Gibson benchmark dataset,
outperforming the previous state-of-the-art. We also perform comprehensive
ablation studies to identify the current bottleneck in the object navigation
task.

该篇论文介绍了一种模块化的训练免费方案，利用经典的 V-SLAM 框架构建结构化场景表示，并注入语义和统计信息，通过感知物体类别和语义信息指导智能体进行探索和导航任务。

不训练你的龙：利用语义前沿进行无需训练的物体目标导航

How To Not Train Your Dragon: Training-free Embodied Object Goal  Navigation with Semantic Frontiers

Estimating absolute camera orientations is essential for attitude estimation
tasks. An established approach is to first carry out visual odometry (VO) or
visual SLAM (V-SLAM), and retrieve the camera orientations (3 DOF) from the
camera poses (6 DOF) estimated by VO or V-SLAM. One drawback of this approach,
besides the redundancy in estimating full 6 DOF camera poses, is the dependency
on estimating a map (3D scene points) jointly with the 6 DOF poses due to the
basic constraint on structure-and-motion. To simplify the task of absolute
orientation estimation, we formulate the monocular rotational odometry problem
and devise a fast algorithm to accurately estimate camera orientations with
2D-2D feature matches alone. Underpinning our system is a new incremental
rotation averaging method for fast and constant time iterative updating.
Furthermore, our system maintains a view-graph that 1) allows solving loop
closure to remove camera orientation drift, and 2) can be used to warm start a
V-SLAM system. We conduct extensive quantitative experiments on real-world
datasets to demonstrate the accuracy of our incremental camera orientation
solver. Finally, we showcase the benefit of our algorithm to V-SLAM: 1) solving
the known rotation problem to estimate the trajectory of the camera and the
surrounding map, and 2)enabling V-SLAM systems to track pure rotational
motions.

通过 2D-2D 特征匹配技术，利用旋转平均算法设计一种可快速而又准确地估算单目相机旋转方向的系统，该系统通过维护一个视图图形并解决旋转问题来估算摄像机轨迹和地图，并驱动 V-SLAM 系统跟踪纯旋转运动。