For Lifelong SLAM, one has to deal with temporary localization failures, e.g., induced by kidnapping. We achieve this by starting a new map and merging it with the previous map as soon as relocalization succeeds. Since relocalization methods are fallible, it can happen that such a merge is invalid, e.g., due to perceptual aliasing. To address this issue, we propose methods to detect and undo invalid merges. These methods compare incoming scans with scans that were previously merged into the current map and consider how well they agree with each other. Evaluation of our methods takes place using a dataset that consists of multiple flat and office environments, as well as the public MIT Stata Center dataset. We show that methods based on a change detection algorithm and on comparison of gridmaps perform well in both environments and can be run in real-time with a reasonable computational cost.
University of Freiburg（弗赖堡大学）；University of Technology Nuremberg（纽伦堡工业大学）
Robots operating in the open world encounter various different environments that can substantially differ from each other. This domain gap also poses a challenge for Simultaneous Localization and Mapping (SLAM) being one of the fundamental tasks for navigation. In particular, learning-based SLAM methods are known to generalize poorly to unseen environments hindering their general adoption. In this work, we introduce the novel task of continual SLAM extending the concept of lifelong SLAM from a single dynamically changing environment to sequential deployments in several drastically differing en…
Lifelong SLAM considers long-term operation of a robot where already mapped locations are revisited many times in changing environments. As a result, traditional graph-based SLAM approaches eventually become extremely slow due to the continuous growth of the graph and the loss of sparsity. Both problems can be addressed by a graph pruning algorithm. It carefully removes vertices and edges to keep the graph size reasonable while preserving the information needed to provide good SLAM results. We propose a novel method that considers geometric criteria for choosing the vertices to be pruned. It i…
Gaussian Robotics（高仙机器人）；Intel Labs China（英特尔中国研究院）；National University of Singapore（新加坡国立大学）；Harbin Institute of Technology（哈尔滨工业大学）
The environment of most real-world scenarios such as malls and supermarkets changes at all times. A pre-built map that does not account for these changes becomes out-of-date easily. Therefore, it is necessary to have an up-to-date model of the environment to facilitate long-term operation of a robot. To this end, this paper presents a general lifelong simultaneous localization and mapping (SLAM) framework. Our framework uses a multiple session map representation, and exploits an efficient map updating strategy that includes map building, pose graph refinement and sparsification. To mitigate th…
The time complexity of making observations and loop closures in a graph-based visual SLAM system is a function of the number of views stored. Clever algorithms, such as approximate nearest neighbor search, can make this function sub-linear. Despite this, over time the number of views can still grow to a point at which the speed and/or accuracy of the system becomes unacceptable, especially in computation- and memory-constrained SLAM systems. However, not all views are created equal. Some views are rarely observed, because they have been created in an unusual lighting condition, or from low qua…
Beijing Jiaotong University（北京交通大学）；Intel Labs China（英特尔中国实验室）；Beihang University（北京航空航天大学）；Tsinghua University（清华大学）；Gaussian Robotics（高斯机器人）；Department of Electronic Engineering and BNRist（电子工程系和BNRist）
Service robots should be able to operate autonomously in dynamic and daily changing environments over an extended period of time. While Simultaneous Localization And Mapping (SLAM) is one of the most fundamental problems for robotic autonomy, most existing SLAM works are evaluated with data sequences that are recorded in a short period of time. In real-world deployment, there can be out-of-sight scene changes caused by both natural factors and human activities. For example, in home scenarios, most objects may be movable, replaceable or deformable, and the visual features of the same place may …
Long-term 3D map management is a fundamental capability required by a robot to reliably navigate in the non-stationary real-world. This paper develops open-source, modular, and readily available LiDAR-based lifelong mapping for urban sites. This is achieved by dividing the problem into successive subproblems: multi-session SLAM (MSS), high/low dynamic change detection, and positive/negative change management. The proposed method leverages MSS and handles potential trajectory error; thus, good initial alignment is not required for change detection. Our change management scheme preserves efficac…
The University of Manchester（曼彻斯特大学）；Intel Labs China（英特尔中国实验室）；Beihang University（北京航空航天大学）
Progress in the last decade has brought about significant improvements in the accuracy and speed of SLAM systems, broadening their mapping capabilities. Despite these advancements, long-term operation remains a major challenge, primarily due to the wide spectrum of perturbations robotic systems may encounter. Increasing the robustness of SLAM algorithms is an ongoing effort, however it usually addresses a specific perturbation. Generalisation of robustness across a large variety of challenging scenarios is not well-studied nor understood. This paper presents a systematic evaluation of the robu…
Carnegie Mellon University（卡内基梅隆大学）；University of California San Diego（加州大学圣地亚哥分校）
We present BioSLAM, a lifelong SLAM framework for learning various new appearances incrementally and maintaining accurate place recognition for previously visited areas. Unlike humans, artificial neural networks suffer from catastrophic forgetting and may forget the previously visited areas when trained with new arrivals. For humans, researchers discover that there exists a memory replay mechanism in the brain to keep the neuron active for previous events. Inspired by this discovery, BioSLAM designs a gated generative replay to control the robot's learning behavior based on the feedback reward…
Energy-Efficient SLAM via Joint Design of Sensing, Communication, and Exploration Speed
Southern University of Science and Technology（南方科技大学）；Harbin Institute of Technoglogy (Shenzhen)（哈尔滨工业大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）；The School of Electronics and Communication Engineering（电子与通信工程学院）；Sun Yat-sen University（中山大学）
Massachusetts Institute of Technology (MIT)（麻省理工学院（MIT））；Northeastern University（东北大学）
Simultaneous localization and mapping (SLAM) is a critical capability in autonomous navigation, but in order to scale SLAM to the setting of "lifelong" SLAM, particularly under memory or computation constraints, a robot must be able to determine what information should be retained and what can safely be forgotten. In graph-based SLAM, the number of edges (measurements) in a pose graph determines both the memory requirements of storing a robot's observations and the computational expense of algorithms deployed for performing state estimation using those observations; both of which can grow unbo…
University of Bristol（布里斯托大学）；Zhejiang University（浙江大学）
We propose a novel end-to-end RGB-D SLAM, iDF-SLAM, which adopts a feature-based deep neural tracker as the front-end and a NeRF-style neural implicit mapper as the back-end. The neural implicit mapper is trained on-the-fly, while though the neural tracker is pretrained on the ScanNet dataset, it is also finetuned along with the training of the neural implicit mapper. Under such a design, our iDF-SLAM is capable of learning to use scene-specific features for camera tracking, thus enabling lifelong learning of the SLAM system. Both the training for the tracker and the mapper are self-supervised…
Instituto de Investigación en Ingeniería de Aragón (I3A)（阿拉贡工程研究院（I3A））；Universidad de Zaragoza（萨拉戈萨大学）
This paper presents ORB-SLAM, a feature-based monocular SLAM system that operates in real time, in small and large, indoor and outdoor environments. The system is robust to severe motion clutter, allows wide baseline loop closing and relocalization, and includes full automatic initialization. Building on excellent algorithms of recent years, we designed from scratch a novel system that uses the same features for all SLAM tasks: tracking, mapping, relocalization, and loop closing. A survival of the fittest strategy that selects the points and keyframes of the reconstruction leads to excellent r…
Loop closure detection is an important building block that ensures the accuracy and robustness of simultaneous localization and mapping (SLAM) systems. Due to their generalization ability, CNN-based approaches have received increasing attention. Although they normally benefit from training on datasets that are diverse and reflective of the environments, new environments often emerge after the model is deployed. It is therefore desirable to incorporate the data newly collected during operation for incremental learning. Nevertheless, simply finetuning the model on new data is infeasible since it…
A Survey on Collaborative SLAM with 3D Gaussian Splatting
Institute of System Integration, Le Quy Don Technical University, Hanoi, 10000, Vietnam（越南吕文技术大学系统整合研究所）；Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology（日本先进科学技术研究院先进科学技术研究生院）；University of Engineering and Technology, Vietnam National University, Hanoi, 10000, Vietnam（越南国家大学工程技术大学）
RoDyn-SLAM: Robust Dynamic Dense RGB-D SLAM with Neural Radiance Fields
School of Data Science, Fudan University（复旦大学数据科学学院）；Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学脑智能科学与技术研究院）；ByteDance（字节跳动）
RoDyn-SLAM 提出一种基于神经辐射场的动态稠密 RGB-D SLAM 框架，通过融合光流与语义的运动掩码滤除动态干扰，并采用分而治之位姿优化与边缘扭曲损失，在准确性和鲁棒性上达到最先进水平。
University of Science and Technology of China（中国科学技术大学）
Simultaneous localization and mapping (SLAM) based on laser sensors has been widely adopted by mobile robots and autonomous vehicles. These SLAM systems are required to support accurate localization with limited computational resources. In particular, point cloud registration, i.e., the process of matching and aligning multiple LiDAR scans collected at multiple locations in a global coordinate framework, has been deemed as the bottleneck step in SLAM. In this paper, we propose a feature filtering algorithm, PFilter, that can filter out invalid features and can thus greatly alleviate this bottl…
Robust Lifelong Indoor LiDAR Localization using the Area Graph
ShanghaiTech University（上海科技大学）；School of Information Science and Technology (SIST)（信息科学与技术学院）；Key Laboratory of Intelligent Perception and Human-Machine Collaboration（智能感知与人机协同重点实验室）
本文提出基于 Area Graph 的终身室内 3D LiDAR 定位方法，通过语义建筑特征、点云滤波及加权点到线 ICP 实现鲁棒全局定位与位姿跟踪。
