Query: static environment odometry

title: Research on a line expanded visual odometry in dynamic environment
abstract: The Point feature and line feature have been widely used in visual SLAM(simultaneous localization and mapping) algorithm. But most of these methods assume that the environments are static, ignoring that there are often dynamic objects in real world, which can degrade the SLAM performance. In order to solve this problem, a line expanded visual odometry is proposed. It calculates optical flow between two adjacent frames to identify and eliminate dynamic point features in dynamic objects, and use the rest of point features to find the collinear relationship to expand line features for visual SLAM algorithm based on point features. Final it use the rest of point features and line features to estimate the camera pose. The proposed method not only reduces the influence of dynamic objects, but also avoids the tracking failure caused by few point features. The experiments are carried out on a TUM dataset. Compared with state of the art methods like ORB (oriented FAST and rotated BRIEF) method and ORB add optical flow method, the results demonstrate that the proposed method reduces the tracking error and improve the robustness and accuracy of visual odometry in dynamic environments.

title: Improved Visual Odometry Based on SSD Algorithm in Dynamic Environment
abstract: In recent years, Simultaneous Localization and Mapping (SLAM) have made great progress in the field of robots. However, the majority of SLAM algorithms are currently designed and processed in static environments. In the dynamic environment, due to the existence of moving objects, the SLAM system is easy to mismatching the pose estimation, which will affect its positioning accuracy and mapping accuracy. In this paper, the traditional Oriented FAST and Rotated Brief (ORB) SLAM2 system is improved to deal with these problems. Firstly, the Single Shot MultiBox Detector (SSD)algorithm is used to detect the dynamic targets in the scene, and then the multi layer optical flow is constructed to remove the dynamic feature points by optical flow tracking. Secondly, the static feature points in the scene are matched and the pose are calculated to solve the problems caused by a large number of matches, such as large system computation and low operating efficiency. Finally, testing on the TUM datasets shows that the absolute trajectory error and the relative pose error of the improved ORB SLAM2 system are significantly reduced compared to the traditional ORB SLAM2 system.

title: RGB D visual odometry with point and line features in dynamic environment
abstract: Vision based simultaneous localization and mapping (SLAM) technology is the key to realize autonomous navigation of mobile robots. When the robot is in an unfamiliar environment, it usually uses the point features of the surrounding environment to estimate its pose. However, if the feature information in the environment is not rich and there are many dynamic objects, the camera trajectory cannot be accurately estimated. To this end, this paper proposed an RGB D visual odometry that combines point features and line features simultaneously. The dynamic line features are eliminated by calculating the static weight of the line features, and the camera pose is estimated based on the point features and the remaining line features. Compared with other feature based SLAM systems, the performance and accuracy of systematic pose estimation can be improved in the absence of feature points or dynamic environments.

title: Feature Regions Segmentation Based RGB D Visual Odometry in Dynamic Environment
abstract: A novel RGB D visual odometry method for dynamic environment is proposed. Majority of visual odometry systems can only work in static environments, which limits their applications in real world. In order to improve the accuracy and robustness of visual odometry in dynamic environment, a Feature Regions Segmentation algorithm is proposed to resist the disturbance caused by the moving objects. The matched features are divided into different regions to separate the moving objects from the static background. The features in the largest region which belong to the static background are used to estimate the camera pose finally. The effectiveness of our visual odometry method is verified in a dynamic environment of our lab. Furthermore, an exhaustive experimental evaluation is conducted on benchmark datasets including static environments and dynamic environments compared with the state of art visual odometry systems. The accuracy comparison results show that the proposed algorithm outperforms those systems in large scale dynamic environments. Our method tracks the camera movement correctly while others failed. In addition, our method can give the same good performances in static environment. Experiments demonstrate that the proposed RGB D visual odometry can obtain accurate and robust estimation results in dynamic environments.

title: Robust 3 Dimension Point Cloud Mapping in Dynamic Environment Using Point Wise Static Probability Based NDT Scan Matching
abstract: Graph based simultaneous localization and mapping (SLAM) is one of the methods to generate point cloud maps which are used for various applications in autonomous vehicles. Graph based SLAM represents the pose of the vehicle as a node and the odometry between two different nodes as an edge. Among the edge generating methods, scan matching, light detection and ranging (LiDAR) based method, can provide an accurate pose between two nodes based on the high distance accuracy of the LiDAR. However, the point cloud in real driving situations contains numerous moving objects, which degrade the scan matching performance. Therefore, this article defines the static probability which means the likelihood that an acquired point is from a static object, and proposes the weighted normal distribution transformation (NDT) which is achieved by modifying NDT. Weighted NDT is a scan matching algorithm which can reflect the static probability of each point as a weight. The odometry from the weighted NDT is utilized for graph construction to generate a robust point cloud map even in a dynamic environment. Finally, the proposed algorithm was compared with the existing object removal algorithms in two areas: dynamic object classification and scan matching performance. Based on the scan matching results, the accuracy of the point cloud map generated by the proposed algorithm was evaluated with a reference map using high performance global navigation satellite system (GNSS) It was confirmed that the proposed algorithm has higher classification accuracy and lower scan matching error compared with other dynamic object removal methods. The proposed algorithm was able to generate a point cloud map, despite the presence of many dynamic objects, that was similar to a map generated in the absence of dynamic objects in the same environment.

title: Visual Odometry Algorithm Using an RGB D Sensor and IMU in a Highly Dynamic Environment
abstract: This paper proposes a robust visual odometry algorithm using a Kinect style RGB D sensor and inertial measurement unit (IMU) in a highly dynamic environment. Based on SURF (Speed Up Robust Features) descriptor, the proposed algorithm generates 3 D feature points incorporating depth information into RGB color information. By using an IMU, the generated 3 D feature points are rotated in order to have the same rigid body rotation component between two consecutive images. Before calculating the rigid body transformation matrix between the successive images from the RGB D sensor, the generated 3 D feature points are filtered into dynamic or static feature points using motion vectors. Using the static feature points, the rigid body transformation matrix is finally computed by RANSAC (RANdom SAmple Consensus) algorithm. The experiments demonstrate that visual odometry is successfully obtained for a subject and a mobile robot by the proposed algorithm in a highly dynamic environment. The comparative study between proposed method and conventional visual odometry algorithm clearly show the reliability of the approach for computing visual odometry in a highly dynamic environment.

title: Exploration for autonomous 3D voxel mapping of static indoor environments with depth cameras and 2D odometry
abstract: This thesis focuses on the development of an autonomous exploration and 3D mapping algorithm for a mobile service platform in an indoor environment. The designated robot platform is the KUKA omniRob additionally equipped with eight Time of Flight (ToF) cameras and the algorithm is developed and tested within the institute's internal Mobile Robot Environment (MRE) simulator. The Simultaneous Localization and Mapping (SLAM) problem is solved by a metaview registration method, in which range measurements and their corresponding odometry estimate are combined together by the Iterative Closest Point (ICP) algorithm. The algorithm operates on the raw range measurement without any feature generation. The result of the mapping process is a 3D occupancy grid map used for the autonomous exploration tasks. An information gain and frontier based exploration routine is developed, based on a preceding discussion and analysis of literature on the subject. The implemented strategy uses an iterative approach for exploring the complete working space of the robot. Therefore, a collision free, traversable and reachable space is defined. This thesis introduces a 2D exploration grid map as a projection of the 3D occupancy grid map, which serves as an input for the given path planning process. The experiments within the MRE simulation environment demonstrate the applicability of the developed algorithm and give a critical analysis of existing assets and drawbacks. An autonomously working exploration algorithm is developed in this thesis, which provides a framework for further extensions and improvements to facilitate future researches.

title: Dynamic DSO: Direct Sparse Odometry Using Objects Semantic Information for Dynamic Environments
abstract: Traditional Simultaneous Localization and Mapping (SLAM) (with loop closure detection) or Visual Odometry (VO) (without loop closure detection) are based on the static environment assumption. When working in dynamic environments, they perform poorly whether using direct methods or indirect methods (feature points methods) In this paper, Dynamic DSO which is a semantic monocular direct visual odometry based on DSO (Direct Sparse Odometry) is proposed. The proposed system is completely implemented with the direct method, which is different from the most current dynamic systems combining the indirect method with deep learning. Firstly, convolutional neural networks (CNNs) are applied to the original RGB image to generate the pixel wise semantic information of dynamic objects. Then, based on the semantic information of the dynamic objects, dynamic candidate points are filtered out in keyframes candidate points extraction; only static candidate points are reserved in the tracking and optimization module, to achieve accurate camera pose estimation in dynamic environments. The photometric error calculated by the projection points in dynamic region of subsequent frames are removed from the whole photometric error in pyramid motion tracking model. Finally, the sliding window optimization which neglects the photometric error calculated in the dynamic region of each keyframe is applied to obtain the precise camera pose. Experiments on the public TUM dynamic dataset and the modified Euroc dataset show that the positioning accuracy and robustness of the proposed Dynamic DSO is significantly higher than the state of the art direct method in dynamic environments, and the semi dense cloud map constructed by Dynamic DSO is clearer and more detailed.

title: Visual Odometry in Dynamic Environments using Light Weight Semantic Segmentation
abstract: Visual odometry is the method in which a robot tracks its position and orientation using a sequence of images. Feature based visual odometry matches feature between frames and estimates the pose of the robot according to the matched features. These methods typically assume a static environment and relies on statistical methods such as RANSAC to remove outliers such as moving objects. But in highly dynamic environment where majority of the scene is composed of moving objects these methods fail. This paper proposes to use the feature based visual odometry part of ORB SLAM2 RGB D and improve it using DeepLabv3 MobileNetV2 semantic segmentation. The semantic segmentation algorithm is used to segment the image, then extracted feature points that are on pixels of dynamic objects (people) are not tracked. The method is tested on TUM RGBD dataset. Evaluation shows that the proposed algorithm performs significantly better in dynamic scenes compared to the base algorithm, with reduction in Absolute Trajectory Error (ATE) greater than 92.90% compared to the base algorithm in fr3w_xyz, fr3w_rpy and fr3_half sequences. Additionally, when comparing the algorithm that used DeepLabv3 MobileNetV2 to the computationally intensive DeepLabv3 Xception65, the largest increase in ATE was 27% while the computation time is 3 times faster.

title: Rapid Initialization using Relative Pose Constraints in Stereo Visual Inertial Odometry
abstract: In this paper, we propose an initialization method to bootstrap a visual inertial odometry (VIO) without any prior information using relative constraints computed by a stereo camera. Many works on VIO start their algorithm from a static state or a rough initial guess since an accurate initial velocity and attitude are not available at the early stage. Our approach estimates a relative pose of consecutive camera frames from a stereo visual odometry (VO) then formulates least square problems from the IMU preintegration and the pose constraint. This recovers the initial velocity, attitude with respect to the gravity, and biases of an IMU. Also, we build a framework in which a tightly coupled filtering based VIO is booted by the proposed initialization method. We show that the initial state can be estimated with a 6 DOF motion in the simulated environment. Also, our experimental results reveal that 5 seconds with 20 fps stereo images are enough to initialize the filtering based VIO in the public MAV dataset.