A novel outlier removal method for two-dimensional radar odometry

Autonomous navigation of platforms in complex environments has a key role in many applications. However, the environmental conditions could negatively affect the performance of electro-optical sensors. Hence, the idea of using radar odometry has been recently developed. However, it suffers from the presence of outliers in the scene as its electro-optical counterparts. This work presents a method to classify radar echoes as inliers or outliers for two-dimensional radar odometry, based on their range rate and bearing angle. The range rate and bearing angle are in fact combined to give a classification value, different for each target. At each acquisition time, the median of all classification values is computed. Since classification values of stationary targets, i.e. the inliers, cluster around the median, while moving targets, i.e. the outliers, exhibit larger distance from the median, stationary targets and moving targets can be separated. This is also useful for Sense-and-Avoid purposes. The method has been tested in simulated scenario to show effectiveness in detecting outliers and in real case scenario to demonstrate significant improvement in reconstruction of trajectory of platform, keeping the final error around 10% of the travelled distance. Further improvement is envisaged by integrating the method in the target tracking strategy

Low-effort place recognition with WiFi fingerprints using deep learning

Using WiFi signals for indoor localization is the main localization modality of the existing personal indoor localization systems operating on mobile devices. WiFi fingerprinting is also used for mobile robots, as WiFi signals are usually available indoors and can provide rough initial position estimate or can be used together with other positioning systems. Currently, the best solutions rely on filtering, manual data analysis, and time-consuming parameter tuning to achieve reliable and accurate localization. In this work, we propose to use deep neural networks to significantly lower the work-force burden of the localization system design, while still achieving satisfactory results. Assuming the state-of-the-art hierarchical approach, we employ the DNN system for building/floor classification. We show that stacked autoencoders allow to efficiently reduce the feature space in order to achieve robust and precise classification. The proposed architecture is verified on the publicly available UJIIndoorLoc dataset and the results are compared with other solutions

Robot Localization in an Agricultural Environment

Localization and Mapping of autonomous robots in an harsh and unstable environment such as a steep slope vineyard is a challenging research topic. The commonly used Dead Reckoning systems can fail due to the harsh conditions of the terrain and the accurate Global Position System can be considerably noisy or not always available. Agriculture is moving towards a precision agriculture, with advanced monitoring systems and wireless sensors networks. These systems and wireless sensors are installed in the crop field and can be considered relevant landmarks for robot localization using different types of technologies.In this work the performance of Pozyx, a low cost Time-of-flight system with Ultra-Wide Bandwidth (UWB) technology, is studied and implemented on a real robot range-based localization system. Firstly the error of both the range-only system and the embedded localization algorithm of the sensor is characterized. Then the range measurements are filtered with an EKF algorithm to output the robot pose and finally compared with the localization algorithm of the sensor.The obtained results are presented and compared with previous works showing an increased redundancy of the robot localization estimation. The UWB is proved to offer a good solution for a harsh environment as the agricultural one since its range-measurements are not much impacted by the atmospheric conditions. The discussion also allows to present formulations for better results of Beacons Mapping Procedure (BMP) required for accurate and reliable localization systems

Augmentation of Visual Odometry using Radar

As UAVs become viable for more applications, pose estimation continues to be critical. All UAVs need to know where they are at all times, in order to avoid disaster. However, in the event that UAVs are deployed in an area with poor visual conditions, such as in many disaster scenarios, many localization algorithms have difficulties working. This thesis presents VIL-DSO, a visual odometry method as a pose estimation solution, combining several different algorithms in order to improve pose estimation and provide metric scale. This thesis also presents a method for automatically determining an accurate physical transform between radar and camera data, and in doing so, allow for the projection of radar information into the image plane. Finally, this thesis presents EVIL-DSO, a method for localization that fuses visual-inertial odometry with radar information. The proposed EVIL-DSO algorithm uses radar information projected into the image plane in order to create a depth map for odometry to directly observe depth of features, which can then be used as part of the odometry algorithm to remove the need to perform costly depth estimations. Trajectory analysis of the proposed algorithm on outdoor data, compared to differential GPS data, shows that the proposed algorithm is more accurate in terms of root-mean-square error, as well as having a lower percentage of scale error. Runtime analysis shows that the proposed algorithm updates more frequently than other, similar, algorithms

A Comprehensive Review on Autonomous Navigation

The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed

A Meta-Review of Indoor Positioning Systems

An accurate and reliable Indoor Positioning System (IPS) applicable to most indoor scenarios has been sought for many years. The number of technologies, techniques, and approaches in general used in IPS proposals is remarkable. Such diversity, coupled with the lack of strict and verifiable evaluations, leads to difficulties for appreciating the true value of most proposals. This paper provides a meta-review that performed a comprehensive compilation of 62 survey papers in the area of indoor positioning. The paper provides the reader with an introduction to IPS and the different technologies, techniques, and some methods commonly employed. The introduction is supported by consensus found in the selected surveys and referenced using them. Thus, the meta-review allows the reader to inspect the IPS current state at a glance and serve as a guide for the reader to easily find further details on each technology used in IPS. The analyses of the meta-review contributed with insights on the abundance and academic significance of published IPS proposals using the criterion of the number of citations. Moreover, 75 works are identified as relevant works in the research topic from a selection of about 4000 works cited in the analyzed surveys

A Study on UWB-Aided Localization for Multi-UAV Systems in GNSS-Denied Environments

Unmanned Aerial Vehicles (UAVs) have seen an increased penetration in industrial applications in recent years. Some of those applications have to be carried out in GNSS-denied environments. For this reason, several localization systems have emerged as an alternative to GNSS-based systems such as Lidar and Visual Odometry, Inertial Measurement Units (IMUs), and over the past years also UWB-based systems. UWB technology has increased its popularity in the robotics field due to its high accuracy distance estimation from ranging measurements of wireless signals, even in non-line-of-sight measurements. However, the applicability of most of the UWB-based localization systems is limited because they rely on a fixed set of nodes, named anchors, which requires prior calibration. In this thesis, we present a localization system based on UWB technology with a built-in collaborative algorithm for the online autocalibration of the anchors. This autocalibration method, enables the anchors to be movable and thus, to be used in ad-doc and dynamic deployments. The system is based on Decawave's DWM1001 UWB transceivers. Compared to Decawave's autopositioning algorithm we drastically reduce the calibration time while increasing accuracy. We provide both experimental measurements and simulation results to demonstrate the usability of this algorithm. We also present a comparison between our UWB-based and other non-GNSS localization systems for UAVs positioning in indoor environments

Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications

Nowadays, the availability of the location information becomes a key factor in today’s communications systems for allowing location based services. In outdoor scenarios, the Mobile Terminal (MT) position is obtained with high accuracy thanks to the Global Positioning System (GPS) or to the standalone cellular systems. However, the main problem of GPS or cellular systems resides in the indoor environment and in scenarios with deep shadowing effect where the satellite or cellular signals are broken. In this paper, we will present a review over different technologies and concepts used to improve indoor localization. Additionally, we will discuss different applications based on different localization approaches. Finally, comprehensive challenges in terms of accuracy, cost, complexity, security, scalability, etc. are presente

Continuous-Time Fixed-Lag Smoothing for LiDAR-Inertial-Camera SLAM

Localization and mapping with heterogeneous multi-sensor fusion have been prevalent in recent years. To adequately fuse multi-modal sensor measurements received at different time instants and different frequencies, we estimate the continuous-time trajectory by fixed-lag smoothing within a factor-graph optimization framework. With the continuous-time formulation, we can query poses at any time instants corresponding to the sensor measurements. To bound the computation complexity of the continuous-time fixed-lag smoother, we maintain temporal and keyframe sliding windows with constant size, and probabilistically marginalize out control points of the trajectory and other states, which allows preserving prior information for future sliding-window optimization. Based on continuous-time fixed-lag smoothing, we design tightly-coupled multi-modal SLAM algorithms with a variety of sensor combinations, like the LiDAR-inertial and LiDAR-inertial-camera SLAM systems, in which online timeoffset calibration is also naturally supported. More importantly, benefiting from the marginalization and our derived analytical Jacobians for optimization, the proposed continuous-time SLAM systems can achieve real-time performance regardless of the high complexity of continuous-time formulation. The proposed multi-modal SLAM systems have been widely evaluated on three public datasets and self-collect datasets. The results demonstrate that the proposed continuous-time SLAM systems can achieve high-accuracy pose estimations and outperform existing state-of-the-art methods. To benefit the research community, we will open source our code at ~\url{https://github.com/APRIL-ZJU/clic}