SLAM In Dynamic Environments Research Articles

Multi-Robot SLAM in Dynamic Environments with Parallel Maps

There is an extensive literature regarding multi-robot simultaneous localization and mapping (MRSLAM). In most part of the research, the environment is assumed to be static, while the dynamic parts of the environment degrade the estimation quality of SLAM algorithms and lead to inherently fragile systems. To enhance the performance and robustness of the SLAM in dynamic environments (SLAMIDE), a novel cooperative approach named parallel-map (p-map) SLAM is introduced in this paper. The objective of the proposed method is to deal with the dynamics of the environment, by detecting dynamic parts and preventing the inclusion of them in SLAM estimations. In this approach, each robot builds a limited map in its own vicinity, while the global map is built through a hybrid centralized MRSLAM. The restricted size of the local maps, bounds computational complexity and resources needed to handle a large scale dynamic environment. Using a probabilistic index, the proposed method differentiates between stationary and moving landmarks, based on their relative positions with other parts of the environment. Stationary landmarks are then used to refine a consistent map. The proposed method is evaluated with different levels of dynamism and for each level, the performance is measured in terms of accuracy, robustness, and hardware resources needed to be implemented. The method is also evaluated with a publicly available real-world data-set. Experimental validation along with simulations indicate that the proposed method is able to perform consistent SLAM in a dynamic environment, suggesting its feasibility for MRSLAM applications.

Dynamic to Static Lidar Scan Reconstruction Using Adversarially Trained Auto Encoder

Accurate reconstruction of static environments from LiDAR scans of scenes containing dynamic objects, which we refer to as Dynamic to Static Translation (DST), is an important area of research in Autonomous Navigation. This problem has been recently explored for visual SLAM, but to the best of our knowledge no work has been attempted to address DST for LiDAR scans. The problem is of critical importance due to wide-spread adoption of LiDAR in Autonomous Vehicles. We show that state-of the art methods developed for the visual domain when adapted for LiDAR scans perform poorly. We develop DSLR, a deep generative model which learns a mapping between dynamic scan to its static counterpart through an adversarially trained autoencoder. Our model yields the first solution for DST on LiDAR that generates static scans without using explicit segmentation labels. DSLR cannot always be applied to real world data due to lack of paired dynamic-static scans. Using Unsupervised Domain Adaptation, we propose DSLR-UDA for transfer to real world data and experimentally show that this performs well in real world settings. Additionally, if segmentation information is available, we extend DSLR to DSLR-Seg to further improve the reconstruction quality. DSLR gives the state of the art performance on simulated and real-world datasets and also shows at least 4× improvement. We show that DSLR, unlike the existing baselines, is a practically viable model with its reconstruction quality within the tolerable limits for tasks pertaining to autonomous navigation like SLAM in dynamic environments.

Visual SLAM in dynamic environments based on object detection

A great number of visual simultaneous localization and mapping (VSLAM) systems need to assume static features in the environment. However, moving objects can vastly impair the performance of a VSLAM system which relies on the static-world assumption. To cope with this challenging topic, a real-time and robust VSLAM system based on ORB-SLAM2 for dynamic environments was proposed. To reduce the influence of dynamic content, we incorporate the deep-learning-based object detection method in the visual odometry, then the dynamic object probability model is added to raise the efficiency of object detection deep neural network and enhance the real-time performance of our system. Experiment with both on the TUM and KITTI benchmark dataset, as well as in a real-world environment, the results clarify that our method can significantly reduce the tracking error or drift, enhance the robustness, accuracy and stability of the VSLAM system in dynamic scenes.

Moving object removal and surface mesh mapping for path planning on 3D terrain

ABSTRACT This paper proposes a method of moving object removal and map building for path planning on 3D terrain. Our method introduces a map representation named surface mesh maps, which is built from 3D LIDAR points using graph-based SLAM with moving object removal and polygon mesh reconstruction. We need moving object removal since moving objects, such as pedestrians, are included in raw maps built by SLAM in dynamic environments. For path planning, it is desirable that only stationary (static) objects remain in the maps. Occupancy voxel filtering can be used to remove moving objects. Stationary objects have high occupancy probability, and moving objects have low occupancy probability. However, due to shallow incidence of the LIDAR beams in 3D space, it is difficult to determine the correct probability of occupancy. Hence, we improve moving object removal using a new inverse measurement model with incident angles and a two-pass scheme to properly calculate the occupancy probability. In addition, we propose a path planning method using a graph search and the graph structures of the surface mesh maps. Path costs are given to the nodes and the arcs of the graph since path planning on 3D terrain requires an appropriate cost calculation. We introduce node costs and arc costs based on slopes, roughness, height differences, and travel distances. Path planning is performed by a graph search using the costs and graph pruning using collision detection and mesh boundary detection. The main contribution of our method is to combine SLAM with moving object removal and a graph search for path planning on large-scale 3D terrain. We conducted experiments by traveling over 5 km in outdoor dynamic environments. The results showed that the proposed method is capable of moving object removal and surface mesh mapping for path planning on 3D terrain.

Coarse Semantic-Based Motion Removal for Robust Mapping in Dynamic Environments

SLAM in dynamic environments is still a severe challenge for most feature-based SLAM systems. Moving objects will lead to terrible errors in the calculation of frame tracking and local mapping. We propose a novel method for keypoints selection to lower the negative effect brought by moving objects during map construction. To address this challenge, we concentrate on the combination of coarse semantic information and a feature-based SLAM system. In this article, a modified CenterNet object detector is proposed as the moving object detection thread for providing coarse semantic information and 2D location. The modified CenterNet can provide a faster and more accurate prediction. For each frame in a sequence, objects in a scenario will be classified into two motion states, non-static and static, according to the category prediction from the moving object detection thread. Then important processing called semantic data association is presented for motion removal in frame tracking thread and local mapping thread. In this way, the keypoints on the non-static objects will not be chosen for calculation. Finally, on this basis, a modified real-time SLAM system is presented. Experimental results in TUM indoor dataset show that the proposed SLAM system outperforms the state-of-the-art ORB-SLAM2 in dynamic environments. Besides that, our method has a better speed-accuracy trade-off than other deep learning-based SLAM systems in dynamic environments.

DDL-SLAM: A Robust RGB-D SLAM in Dynamic Environments Combined With Deep Learning

Visual Simultaneous Localization and Mapping (VSLAM) has developed as the basic ability of robots in past few decades. There are a lot of open-sourced and impressive SLAM systems. However, the majority of the theories and approaches of SLAM systems at present are based on the static scene assumption, which is usually not practical in reality because moving objects are ubiquitous and inevitable under most circumstances. In this paper the DDL-SLAM (Dynamic Deep Learning SLAM) is proposed, a robust RGB-D SLAM system for dynamic scenarios that, based on ORB-SLAM2, adds the abilities of dynamic object segmentation and background inpainting. We are able to detect moving objects utilizing both semantic segmentation and multi-view geometry. Having a static scene map allows inpainting background of the frame which has been obscured by moving objects, therefore the localization accuracy is greatly improved in the dynamic environment. Experiment with a public RGB-D benchmark dataset, the results clarify that DDL-SLAM can significantly enhance the robustness and stability of the RGB-D SLAM system in the highly-dynamic environment.

Improving monocular visual SLAM in dynamic environments: an optical-flow-based approach

Visual Simultaneous Localization and Mapping (visual SLAM) has attracted more and more researchers in recent decades and many state-of-the-art algorithms have been proposed with rather satisfactory performance in static scenarios. However, in dynamic scenarios, the performance of current visual SLAM algorithms degrades significantly due to the disturbance of the dynamic objects. To address this problem, we propose a novel method which uses optical flow to distinguish and eliminate the dynamic feature points from the extracted ones using RGB images as the only input. The static feature points are fed into the visual SLAM system for the camera pose estimation. We integrate our method with the original ORB-SLAM system and validate the proposed method with the challenging dynamic sequences from the TUM dataset and our recorded office dataset. The whole system can work in real time. Qualitative and quantitative evaluations demonstrate that our method significantly improves the performance of ORB-SLAM in dynamic scenarios.

Dynamic-SLAM: Semantic monocular visual localization and mapping based on deep learning in dynamic environment

When working in dynamic environment, traditional SLAM framework performs poorly due to interference from dynamic objects. By taking advantages of deep learning in object detection, a semantic simultaneous localization and mapping framework named Dynamic-SLAM is proposed, in order to solve the problem of SLAM in dynamic environment. First, based on the convolutional neural network, an SSD object detector which combines prior knowledge is constructed to detect dynamic objects in the newly detection thread at semantic level. Then, in view of low recall rate of the existing SSD object detection network, a missed detection compensation algorithm based on the speed invariance in adjacent frames is proposed, which greatly improves the recall rate of detection. Finally, a feature-based visual SLAM system is constructed, which processes the feature points of dynamic objects through a selective tracking algorithm in the tracking thread, to significantly reduce the error of pose estimation caused by incorrect matching. The recall rate of the system is increased from 82.3% to 99.8% compared with the original SSD network. Several experiments show that the localization accuracy of Dynamic-SLAM is higher than the state-of-the-art systems. The system successfully localizes and constructs an accurate environmental map in real-world dynamic environment by using a mobile robot. In sum, our experimental demonstrations verify that Dynamic-SLAM shows improved accuracy and robustness in robot localization and mapping comparing to the state-of-the-art SLAM system in dynamic environment.

Motion removal for reliable RGB-D SLAM in dynamic environments

RGB-D data-based Simultaneous Localization and Mapping (RGB-D SLAM) aims to concurrently estimate robot poses and reconstruct traversed environments using RGB-D sensors. Many effective and impressive RGB-D SLAM algorithms have been proposed over the past years. However, virtually all the RGB-D SLAM systems developed so far rely on the static-world assumption. This is because the SLAM performance is prone to be degraded by the moving objects in dynamic environments. In this paper, we propose a novel RGB-D data-based motion removal approach to address this problem. The approach is on-line and does not require prior-known moving-object information, such as semantics or visual appearances. We integrate the approach into the front end of an RGB-D SLAM system. It acts as a pre-processing stage to filter out data that are associated with moving objects. Experimental results demonstrate that our approach is able to improve RGB-D SLAM in various challenging scenarios.

Research on Mobile Robot SLAM in Dynamic Environment

A SLAMiDE（SLAM in Dynamic Environments) system is designed and realized in the paper, which supplies a holistic framework and a series of implementation methods for mobile robot SLAM in dynamic environments. A uniform target model is proposed in SLAMiDE system. The dynamic targets and static targets and the mobile robot pose are estimated simultaneously, by synthesized the research of the data association and dynamic targets detection and static SLAM based on local maps. Finally, the results of the experimental test prove that the SLAMiDE system can realize dynamic objects detection and mapping and location correctly.

Geometric and Bayesian models for safe navigation in dynamic environments

Autonomous navigation in open and dynamic environments is an important challenge, requiring to solve several difficult research problems located on the cutting edge of the state of the art. Basically, these problems may be classified into three main categories: (a) SLAM in dynamic environments; (b) detection, characterization, and behavior prediction of the potential moving obstacles; and (c) online motion planning and safe navigation decision based on world state predictions. This paper addresses some aspects of these problems and presents our latest approaches and results. The solutions we have implemented are mainly based on the followings paradigms: multiscale world representation of static obstacles based on the wavelet occupancy grid; adaptative clustering for moving obstacle detection inspired on Kohonen networks and the growing neural gas algorithm; and characterization and motion prediction of the observed moving entities using Hidden Markov Models coupled with a novel algorithm for structure and parameter learning.