A General Framework for Lifelong Localization and Mapping in Changing Environment

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 A pre-built map that does not account for these changes becomes out-of-date easily.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 the unbounded increase of memory usage, we propose a map-trimming method based on the Chow-Liu maximum-mutual-information spanning tree. The proposed SLAM framework has been comprehensively validated by over a month of robot deployment in real supermarket environment. Furthermore, we release the dataset collected from the indoor and outdoor changing environment with the hope to accelerate lifelong SLAM research in the community. Our dataset is available at https://github.com/sanduan168/lifelong-SLAM-dataset.

Hierarchical Segment-based Optimization for SLAM

This paper presents a hierarchical segment-based optimization method for Simultaneous Localization and Mapping (SLAM) system. First we propose a reliable trajectory segmentation method that can be used to increase efficiency in the back-end optimization. Then we propose a buffer mechanism for the first time to improve the robustness of the segmentation. During the optimization, we use global information to optimize the frames with large error, and interpolation instead of optimization to update well-estimated frames to hierarchically allocate the amount of computation according to error of each frame. Comparative experiments on the benchmark show that our method greatly improves the efficiency of optimization with almost no drop in accuracy, and outperforms existing high-efficiency optimization method by a large margin.

Robust SLAM Systems: Are We There Yet?

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 robustness of open-source state-of-the-art SLAM algorithms with respect to challenging conditions such as fast motion, non-uniform illumination, and dynamic scenes. The experiments are performed with perturbations present both independently of each other, as well as in combination in long-term deployment settings in unconstrained environments (lifelong operation).

Continual Neural Mapping: Learning An Implicit Scene Representation from Sequential Observations

Recent advances have enabled a single neural network to serve as an implicit scene representation, establishing the mapping function between spatial coordinates and scene properties. In this paper, we make a further step towards continual learning of the implicit scene representation directly from sequential observations, namely Continual Neural Mapping. The proposed problem setting bridges the gap between batch-trained implicit neural representations and commonly used streaming data in robotics and vision communities. We introduce an experience replay approach to tackle an exemplary task of continual neural mapping: approximating a continuous signed distance function (SDF) from sequential depth images as a scene geometry representation. We show for the first time that a single network can represent scene geometry over time continually without catastrophic forgetting, while achieving promising trade-offs between accuracy and efficiency.

A Collaborative Visual SLAM Framework for Service Robots

With the rapid deployment of service robots, a method should be established to allow multiple robots to work in the same place to collaborate and share the spatial information. To this end, we present a collaborative visual simultaneous localization and mapping (SLAM) framework particularly designed for service robot scenarios. With an edge server maintaining a map database and performing global optimization, each robot can register to an existing map, update the map, or build new maps, all with a unified interface and low computation and memory cost. To enable real-time information sharing, an efficient landmark retrieval method is proposed to allow each robot to get nearby landmarks observed by others. The framework is general enough to support both RGB-D and monocular cameras, as well as robots with multiple cameras, taking the rigid constraints between cameras into consideration. The proposed framework has been fully implemented and verified with public datasets and live experiments.

RaP-Net: A Region-wise and Point-wise Weighting Network to Extract Robust Keypoints for Indoor Localization

Image keypoint extraction is an important step for visual localization. The localization in indoor environment is challenging for that there may be many unreliable features on dynamic or repetitive objects. Such kind of reliability cannot be well learned by existing Convolutional Neural Network (CNN) based feature extractors. We propose a novel network, RaP-Net, which explicitly addresses feature invariability with a region-wise predictor, and combines it with a point-wise predictor to select reliable keypoints in an image. We also build a new dataset, OpenLORIS-Location, to train this network. The dataset contains 1553 indoor images with location labels. There are various scene changes between images on the same location, which can help a network to learn the invariability in typical indoor scenes. Experimental results show that the proposed RaP-Net trained with the OpenLORIS-Location dataset significantly outperforms existing CNN-based keypoint extraction algorithms for indoor localization. The code and data are available at https://github.com/ivipsourcecode/RaP-Net.

DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features

A robust and efficient Simultaneous Localization and Mapping (SLAM) system is essential for robot autonomy. For visual SLAM algorithms, though the theoretical framework has been well established for most aspects, feature extraction and association is still empirically designed in most cases, and can be vulnerable in complex environments. This paper shows that feature extraction with deep convolutional neural networks (CNNs) can be seamlessly incorporated into a modern SLAM framework. The proposed SLAM system utilizes a state-of-the-art CNN to detect keypoints in each image frame, and to give not only keypoint descriptors, but also a global descriptor of the whole image. These local and global features are then used by different SLAM modules, resulting in much more robustness against environmental changes and viewpoint changes compared with using hand-crafted features. We also train a visual vocabulary of local features with a Bag of Words (BoW) method. Based on the local features, global features, and the vocabulary, a highly reliable loop closure detection method is built. Experimental results show that all the proposed modules significantly outperforms the baseline, and the full system achieves much lower trajectory errors and much higher correct rates on all evaluated data. Furthermore, by optimizing the CNN with Intel OpenVINO toolkit and utilizing the Fast BoW library, the system benefits greatly from the SIMD (single-instruction-multiple-data) techniques in modern CPUs. The full system can run in real-time without any GPU or other accelerators. The code is public at https://github.com/ivipsourcecode/dxslam.

OpenLORIS-Object: A Dataset and Benchmark towards Lifelong Object Recognition

The recent breakthroughs in computer vision have benefited from the availability of large representative datasets (e.g. ImageNet and COCO) for training. Yet, robotic vision poses unique challenges for applying visual algorithms developed from these standard computer vision datasets due to their implicit assumption over non-varying distributions for a fixed set of tasks. Fully retraining models each time a new task becomes available is infeasible due to computational, storage and sometimes privacy issues, while na\"{i}ve incremental strategies have been shown to suffer from catastrophic forgetting. It is crucial for the robots to operate continuously under open-set and detrimental conditions with adaptive visual perceptual systems, where lifelong learning is a fundamental capability. However, very few datasets and benchmarks are available to evaluate and compare emerging techniques. To fill this gap, we provide a new lifelong robotic vision dataset ("OpenLORIS-Object") collected via RGB-D cameras mounted on mobile robots. The dataset embeds the challenges faced by a robot in the real-life application and provides new benchmarks for validating lifelong object recognition algorithms. Moreover, we have provided a testbed of $9$ state-of-the-art lifelong learning algorithms. Each of them involves $48$ tasks with $4$ evaluation metrics over the OpenLORIS-Object dataset. The results demonstrate that the object recognition task in the ever-changing difficulty environments is far from being solved and the bottlenecks are at the forward/backward transfer designs. Our dataset and benchmark are publicly available at \href{https://lifelong-robotic-vision.github.io/dataset/Data_Object-Recognition.html}{\underline{this url}}.

Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM

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 be significantly different in some successive days. Such out-of-sight dynamics pose great challenges to the robustness of pose estimation, and hence a robot's long-term deployment and operation. To differentiate the forementioned problem from the conventional works which are usually evaluated in a static setting in a single run, the term lifelong SLAM is used here to address SLAM problems in an ever-changing environment over a long period of time. To accelerate lifelong SLAM research, we release the OpenLORIS-Scene datasets. The data are collected in real-world indoor scenes, for multiple times in each place to include scene changes in real life. We also design benchmarking metrics for lifelong SLAM, with which the robustness and accuracy of pose estimation are evaluated separately. The datasets and benchmark are available online at https://lifelong-robotic-vision.github.io/dataset/scene.