Localization In Dynamic Environments Research Articles

Enhanced Unmanned Aerial Vehicle Localization in Dynamic Environments Using Monocular Simultaneous Localization and Mapping and Object Tracking

Enhanced Unmanned Aerial Vehicle Localization in Dynamic Environments Using Monocular Simultaneous Localization and Mapping and Object Tracking

VIS-SLAM: A Real-Time Dynamic SLAM Algorithm Based on the Fusion of Visual, Inertial, and Semantic Information

A deep learning-based Visual Inertial SLAM technique is proposed in this paper to ensure accurate autonomous localization of mobile robots in environments with dynamic objects. Addressing the limitations of real-time performance in deep learning algorithms and the poor robustness of pure visual geometry algorithms, this paper presents a deep learning-based Visual Inertial SLAM technique. Firstly, a non-blocking model is designed to extract semantic information from images. Then, a motion probability hierarchy model is proposed to obtain prior motion probabilities of feature points. For image frames without semantic information, a motion probability propagation model is designed to determine the prior motion probabilities of feature points. Furthermore, considering that the output of inertial measurements is unaffected by dynamic objects, this paper integrates inertial measurement information to improve the estimation accuracy of feature point motion probabilities. An adaptive threshold-based motion probability estimation method is proposed, and finally, the positioning accuracy is enhanced by eliminating feature points with excessively high motion probabilities. Experimental results demonstrate that the proposed algorithm achieves accurate localization in dynamic environments while maintaining real-time performance.

Resilient Cooperative Localization Based on Factor Graphs for Multirobot Systems

With the advancement of intelligent perception in multirobot systems, cooperative localization in dynamic environments has become a critical component. However, existing multirobot cooperative localization systems still fall short in meeting high-precision localization requirements in Global Navigation Satellite System (GNSS)-denied environments. In this paper, we propose a factor-graph-based resilient cooperative localization (FG-RCL) algorithm for multirobot systems. This algorithm integrates measurements from visual sensors and Ultra-WideBand (UWB) to achieve accurate cooperative state estimation—overcoming the visibility issues of visual sensors within limited fields of view. We utilize the Joint Probabilistic Data Association (JPDA) algorithm to calculate the corresponding probabilities of multiple visual detection measurements between robots and assign them to their respective edges in the factor graph, thereby addressing the data association challenges in visual detection measurements. Finally, simulation results demonstrate that the proposed algorithm significantly reduces the influence of visual detection measurement interference on the performance of cooperative localization. Experimental results indicate that the proposed algorithm outperforms UWB-based and vision-based methods in terms of localization accuracy. The system is implemented using a factor-graph-based optimization approach, and it exhibits scalability and enables plug-and-play for sensors. Furthermore, it demonstrates resilience in abnormal situations.

UDS-SLAM: real-time robust visual SLAM based on semantic segmentation in dynamic scenes

PurposeExisting Simultaneous Localization and Mapping (SLAM) algorithms have been relatively well developed. However, when in complex dynamic environments, the movement of the dynamic points on the dynamic objects in the image in the mapping can have an impact on the observation of the system, and thus there will be biases and errors in the position estimation and the creation of map points. The aim of this paper is to achieve more accurate accuracy in SLAM algorithms compared to traditional methods through semantic approaches.Design/methodology/approachIn this paper, the semantic segmentation of dynamic objects is realized based on U-Net semantic segmentation network, followed by motion consistency detection through motion detection method to determine whether the segmented objects are moving in the current scene or not, and combined with the motion compensation method to eliminate dynamic points and compensate for the current local image, so as to make the system robust.FindingsExperiments comparing the effect of detecting dynamic points and removing outliers are conducted on a dynamic data set of Technische Universität München, and the results show that the absolute trajectory accuracy of this paper's method is significantly improved compared with ORB-SLAM3 and DS-SLAM.Originality/valueIn this paper, in the semantic segmentation network part, the segmentation mask is combined with the method of dynamic point detection, elimination and compensation, which reduces the influence of dynamic objects, thus effectively improving the accuracy of localization in dynamic environments.

Development of RGB-D simultaneous localization and mapping in dynamic environments based on motion removal and dense map reconstruction

The state-of-art simultaneous localization and mapping (SLAM) methods have been demonstrated of satisfactory results in static environments. However, most of the existing methods cannot be directly applied in dynamic environments to account for moving objects. To solve the practical problem of SLAM in dynamic environments, an RGB-D SLAM method is developed based on moving objects removal and dense map reconstruction. Specifically, deep learning-based instance segmentation is performed to obtain the semantic information which is furthermore combined with multiview geometry theory to detect possible moving objects. Kalman filter and feature fusion algorithms are further used for moving objects tracking to improve the detection accuracy. Then the camera pose is estimated using the static feature points after motion removal to realize reliable localization in dynamic environments. Out of concern for the infeasibility of navigation and perception with sparse feature maps, each-single-view of dense map is constructed using the RGB image and the depth map collected by the RGB-D camera after motion removal. The dense environmental map is finally reconstructed through the registration of point cloud of selected key image frames. Experiments are conducted on public datasets and real scene data to evaluate the performance of the proposed method. Compared with the state-of-art method ORB-SLAM2, the absolute trajectory error and the relative pose error obtained by the proposed method are significantly reduced by at least 91%, and the localization accuracy reaches 0.001 m.

A Novel Robust Inertial and Ultra-Short Baseline Integrated Navigation Strategy Under the Influence of Motion Effect

Ultra-short baseline (USBL) is a mature navigation method for underwater applications. Acoustic outliers and the slant distance calculation error are the main challenges for improving positioning accuracy. The latter, in particular, is often overlooked. In this paper, the slant distance calculation errors due to the motion effect and acoustic outliers will be mainly investigated. To address the problem, the influence of the motion effect on localization in dynamic environments is analyzed first. Aiming at the two challenges, a new measurement model based on the round trip time delay and bearing angles is derived, which can avoid the influence of the motion effect. Besides, to improve the robustness against outliers, a novel robust Kalman filter is designed based on the mixture distribution model. Simulation tests prove that the designed Kalman filter can improve the positioning accuracy in a dynamic environment and overcome the influence of motion effect on slant distance calculation. The proposed method also plays superior performance than the traditional ones under the influence of outliers. The algorithm has also been verified by the river test, and it has better robustness and higher positioning accuracy under dynamic conditions.

RGB-D Inertial Odometry for a Resource-Restricted Robot in Dynamic Environments

Current simultaneous localization and mapping (SLAM) algorithms perform well in static environments but easily fail in dynamic environments. Recent works introduce deep learning-based semantic information to SLAM systems to reduce the influence of dynamic objects. However, it is still challenging to apply a robust localization in dynamic environments for resource-restricted robots. This paper proposes a real-time RGB-D inertial odometry system for resource-restricted robots in dynamic environments named Dynamic-VINS. Three main threads run in parallel: object detection, feature tracking, and state optimization. The proposed Dynamic-VINS combines object detection and depth information for dynamic feature recognition and achieves performance comparable to semantic segmentation. Dynamic-VINS adopts grid-based feature detection and proposes a fast and efficient method to extract high-quality FAST feature points. IMU is applied to predict motion for feature tracking and moving consistency check. The proposed method is evaluated on both public datasets and real-world applications and shows competitive localization accuracy and robustness in dynamic environments. Yet, to the best of our knowledge, it is the best-performance real-time RGB-D inertial odometry for resource-restricted platforms in dynamic environments for now. The proposed system is open source at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/HITSZ-NRSL/Dynamic-VINS.git</uri>

The Adaptive Fingerprint Localization in Dynamic Environment

Indoor localization service is an indispensable part of modern intelligent life, among which Wi-Fi based fingerprint localization system is popular in indoor positioning researches due to its advantages of low cost and widely deployment. However, Wi-Fi based localization system is susceptible to dynamic environment, and fingerprint collection and updating are time-consuming and labor-intensive. To address this problem, we propose a novel positioning framework based on multiple transfer learning fusion using Generalized Policy Iteration (GPI). Firstly, a 1-Dimension Convolutional Autoencoder (1-D CAE) is designed to extract features from one-dimensional fingerprint data; similar to Convolutional Neural Network (CNN), it can not only pay more attention to the information of different dimensions of fingerprints, but also compress redundant information and reduce noise. After that, Domain Adversarial Neural Network (DANN) and Passive Aggressive (PA) algorithm are fused to train localization model based on unlabeled fingerprint of target domain using the theory of GPI in offline stage. Finally, the model is fine-tuned with unlabeled fingerprints and few labeled fingerprints in daily online predictions to improve the performance of the localization system. Various evaluations in five typical scenarios validate the effectiveness of proposed algorithm in dynamic environment, with low tendency, easy recalibration, long-term stabilization high accuracy and so on.

A Robust Localization Method in Indoor Dynamic Environment

Localization is one of the core technologies for mobile robots to achieve full autonomous movement, and is a prerequisite for other autonomous tasks. The robot working environment is dynamic in most cases, so the localization algorithm must overcome the effects of dynamic changes in the environment. The paper proposed a localization algorithm that allows the robot to perform robust and life-long localization in dynamic environment. The algorithm filter out high-dynamic objects and update semi-static object on the map at the same time, it can also use the information provided in semi-static objects to improve localization performance. In this paper, the processing of dynamic objects is divided into two parts: filtering of high-dynamic objects and updating of semi-static objects. For high dynamic object filtering, a dynamic object detection method combining a delay comparison method and a tracking method is proposed by observed the characteristics of localization system; For the update of semi-static objects, this paper uses the pose graph optimization and occupancy map to implement the dynamic update of the map. The combination of the two methods allows the robot to achieve long-term stable localization in a dynamic environment. The experimental results demonstrate that the proposed method allows the robot achieve long-term localization, overcome the effects of high-dynamic objects and keeping the map always consistent with the environment.

Adaptive Device-Free Localization in Dynamic Environments Through Adaptive Neural Networks

Device-free localization based on WiFi Channel State Information (CSI) has attracted considerable attention in recent years. Many of the schemes are learning-based, among which CSI fingerprinting based on deep learning is the most promising. Learning-based schemes assume the distributions of CSI fingerprints keep relatively stable over time. However, this assumption often does not hold. As WiFi signals are prone to be influenced by various environmental factors, CSI fingerprints vary significantly with environmental changes. Consequently, the localization model built for the original environment degrades dramatically in the changed environment. Recalibrating the whole area of interest is labor-intensive and time-consuming. This is a major challenge of device-free WiFi localization. To mitigate this issue, we propose the method AdapLoc, aiming to adapt the original localization model to the changed environment with significantly reduced recalibration effort. AdapLoc is based on one-dimensional Convolutional Neural Network (1D-CNN) and exploits Domain Adaptation (DA) with Semantic Alignment (SA) to achieve adaptation. Extensive evaluations in four real-world single-link testbeds with multiple environmental changes demonstrate the effectiveness of AdapLoc in coping with localization in dynamic environments, outperforming the existing work with respect to localization and adaptation.

Robust Visual Localization in Dynamic Environments Based on Sparse Motion Removal

Visual localization has been well studied in recent decades and applied in many fields as a fundamental capability in robotics. However, the success of the state of the arts usually builds on the assumption that the environment is static. In dynamic scenarios where moving objects are present, the performance of the existing visual localization systems degrades a lot due to the disturbance of the dynamic factors. To address this problem, we propose a novel sparse motion removal (SMR) model that detects the dynamic and static regions for an input frame based on a Bayesian framework. The similarity between the consecutive frames and the difference between the current frame and the reference frame are both considered to reduce the detection uncertainty. After the detection process is finished, the dynamic regions are eliminated while the static ones are fed into a feature-based visual simultaneous localization and mapping (SLAM) system for further visual localization. To verify the proposed method, both qualitative and quantitative experiments are performed and the experimental results have demonstrated that the proposed model can significantly improve the accuracy and robustness for visual localization in dynamic environments. Note to Practitioners —This article was motivated by the visual localization problem in dynamic environments. Visual localization is well applied in many robotic fields such as path planning and exploration as the basic capability for a mobile robot. In the GPS-denied environments, one robot needs to localize itself through perceiving the unknown environment based on a visual sensor. In real-world scenes, the existence of the moving objects will significantly degrade the localization accuracy, which makes the robot implementation unreliable. In this article, an SMR model is designed to handle this problem. Once receiving a frame, the proposed model divides it into dynamic and static regions through a Bayesian framework. The dynamic regions are eliminated, while the static ones are maintained and fed into a feature-based visual SLAM system for further visual localization. The proposed method greatly improves the localization accuracy in dynamic environments and guarantees the robustness for robotic implementation.

Fast and robust visual odometry with a low-cost IMU in dynamic environments

PurposeTo realize stable and precise localization in the dynamic environments, the authors propose a fast and robust visual odometry (VO) approach with a low-cost Inertial Measurement Unit (IMU) in this study.Design/methodology/approachThe proposed VO incorporates the direct method with the indirect method to track the features and to optimize the camera pose. It initializes the positions of tracked pixels with the IMU information. Besides, the tracked pixels are refined by minimizing the photometric errors. Due to the small convergence radius of the indirect method, the dynamic pixels are rejected. Subsequently, the camera pose is optimized by minimizing the reprojection errors. The frames with little dynamic information are selected to create keyframes. Finally, the local bundle adjustment is performed to refine the poses of the keyframes and the positions of 3-D points.FindingsThe proposed VO approach is evaluated experimentally in dynamic environments with various motion types, suggesting that the proposed approach achieves more accurate and stable location than the conventional approach. Moreover, the proposed VO approach works well in the environments with the motion blur.Originality/valueThe proposed approach fuses the indirect method and the direct method with the IMU information, which improves the localization in dynamic environments significantly.

Robust RGB-D visual odometry based on edges and points

Localization in unknown environments is a fundamental requirement for robots. Egomotion estimation based on visual information is a hot research topic. However, most visual odometry (VO) or visual Simultaneous Localization and Mapping (vSLAM) approaches assume static environments. To achieve robust and precise localization in dynamic environments, we propose a novel VO based on edges and points for RGB-D cameras. In contrast to dense motion segmentation, sparse edge alignment with distance transform (DT) errors is adopted to detect the states of image areas. Features in dynamic areas are ignored in egomotion estimation with reprojection errors. Meanwhile, static weights calculated by DT errors are added to pose estimation. Furthermore, local bundle adjustment is utilized to improve the consistencies of the local map and the camera localization. The proposed approach can be implemented in real time. Experiments are implemented on the challenging sequences of the TUM RGB-D dataset. The results demonstrate that the proposed robust VO achieves more accurate and more stable localization than the state-of-the-art robust VO or SLAM approaches in dynamic environments.

Fast Occupancy Grid Filtering Using Grid Cell Clusters From LIDAR and Stereo Vision Sensor Data

Occupancy grid filtering is important for navigation and localization in dynamic environments. The techniques that have been proposed in the past research, such as the Bayesian occupancy filter and the sequential Monte Carlo Bayesian occupancy filter, involve the independent consideration of all grid cells and, thus, are inaccurate and computationally expensive. To solve this problem, we propose a fast occupancy grid filtering method that uses a grid state map. We apply a superpixel-based clustering concept called a grid cell cluster (GCC) to 3D particles measured using light detection and ranging and dense depth maps extracted from a stereo vision sensor. The GCC locates a connection among similar data items according to certain criteria. In our proposed method, a GCC consisting of several grid cells has one dynamic state. Thus, we can reduce the computational cost and the number of computational errors by using the same prediction matrices is generated from the grid cells belonging to an object. Our method was evaluated on the KITTI benchmark data set. The results showed that the proposed method was faster (by approximately 38.9%) than Mekahanachas method and approximately 12% more accurate (on the intersection-over-union metric) than prevalent techniques.

Autonomous Robot Navigation in Highly Populated Pedestrian Zones

In the past, there has been a tremendous amount of progress in the area of autonomous robot navigation, and a large variety of robots have been developed that demonstrated robust navigation capabilities indoors, in nonurban outdoor environments, or on roads; relatively few approaches have focused on navigation in urban environments such as city centers. Urban areas, however, introduce numerous challenges for autonomous robots as they are rather unstructured and dynamic. In this paper, we present a navigation system for mobile robots designed to operate in crowded city environments and pedestrian zones. We describe the different components of this system, including a simultaneous localization and mapping module for dealing with huge maps of city centers, a planning component for inferring feasible paths, taking into account the traversability and type of terrain, a module for accurate localization in dynamic environments, and the means for calibrating and monitoring the platform. Our navigation system has been implemented and tested in several large‐scale field tests, in which a real robot autonomously navigated over several kilometers in a complex urban environment. This also included a public demonstration, during which the robot autonomously traveled along a more than 3‐km‐long route through the city center of Freiburg, Germany.

Omnidirectional vision scan matching for robot localization in dynamic environments

The localization problem for an autonomous robot moving in a known environment is a well-studied problem which has seen many elegant solutions. Robot localization in a dynamic environment populated by several moving obstacles, however, is still a challenge for research. In this paper, we use an omnidirectional camera mounted on a mobile robot to perform a sort of scan matching. The omnidirectional vision system finds the distances of the closest color transitions in the environment, mimicking the way laser rangefinders detect the closest obstacles. The similarity of our sensor with classical rangefinders allows the use of practically unmodified Monte Carlo algorithms, with the additional advantage of being able to easily detect occlusions caused by moving obstacles. The proposed system was initially implemented in the RoboCup Middle-Size domain, but the experiments we present in this paper prove it to be valid in a general indoor environment with natural color transitions. We present localization experiments both in the RoboCup environment and in an unmodified office environment. In addition, we assessed the robustness of the system to sensor occlusions caused by other moving robots. The localization system runs in real-time on low-cost hardware.