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This paper presents an innovative multi-sensor relative navigation module conceived to enable close-proximity operations for on-orbit servicing of small satellites. The module is composed of a Near-Infrared Laser Range Finder, acting both as range measurer and illuminator, and a monocular camera able to detect a set of reflecting fiducial markers installed on the target for pose estimation. Sensor fusion is performed in several points of the proposed processing pipeline, first following a cross-cueing logic in which the range aids the pose determination process, and later through the integration of both measurements into a Multiplicative Extended Kalman Filter for relative state estimation. The entire relative navigation architecture is first tested by numerically reproducing a close-range rendezvous trajectory of a chaser spacecraft moving toward the target, and using the open-source software Blender for synthetic image generation. Simulations are aimed at determining nominal relative state estimation accuracy (also considering variable sensors' acquisition rates) as well as performance under non-nominal conditions (e.g., due to a temporary loss of measurements). Focusing on the image processing and pose estimation pipeline, an in-depth analysis of the computational efficiency is then carried out by running the proposed algorithms on an embedded processing unit (i.e., the Raspberry Pi 4 Model B) using both synthetic data and real images collected within a dedicated experimental testbed. Numerical and experimental results demonstrate capability to attain pose and relative state estimates in line with accuracy requirements to ensure autonomous and safe close-proximity operations, e.g., up to sub-cm and sub-degree error levels in relative position and attitude respectively. In addition, the algorithm's computational runtime has shown to be compatible with real-time implementation at an acquisition frequency of 2 Hz. • An original architecture for spacecraft relative navigation in close proximity is proposed. • It relies on optical measurements from a monocular camera and a laser range finder. • Circular retroreflective markers are mounted on the target satellite. • Numerical and experimental tests show sub-cm and sub-degree error levels in pose estimation. • Computational efficiency suitable for real time implementation on embedded hardware is proven. [ABSTRACT FROM AUTHOR]

With the popularization of indoor pedestrian positioning systems in recent years, many 3D indoor map-building methods and stereoscopic maps for visualization purposes have been developed. This study presents a human-portable 3D simultaneous localization and mapping (SLAM) system that can adapt to various environmental scenarios. Based on RGB-D SLAM and IMU/laser SLAM, we propose a sensor fusion SLAM algorithm that combines the advantages of these two algorithms and fuses Microsoft Kinect, a Hokuyo laser range finder (LRF), and inertial measurement unit (IMU) sensors to implement 3D positioning and mapping. In terms of positioning, the data from the IMU is used to estimate the user’s velocity and attitude. To correct drift from the inertial sensor, the update procedure of the extended Kalman filter in this system mainly depends on displacement, which is estimated from the Kinect sensor. When the displacement estimated from the Kinect sensor is judged as a failure, the walking velocity estimated from the LRF is used to update the extended Kalman filter. Finally, the colored point cloud extracted from the Kinect is used to create dense 3D environmental maps. Experimental results for three different scenarios prove that our proposed algorithm surpasses the other two. [ABSTRACT FROM AUTHOR]

A three-dimensional (3D) laser imaging system based on time of flight is proposed, based on the human retina structure. The system obtains 3D images with space-variant resolution, and we further establish mathematical models of the system and carried out simulative comparison between space-variant structure (SVS) and space-invariant structure (SIS). The system based on SVS produces significant improvements over traditional system based on SIS in the following aspects: (1) The system based on SVS uses less pixels than that based on SIS under the same field of view (FOV) and resolution. Therefore, this property is more suitable for uses in situations that require high speed and large volume data processing. (2) The system based on SVS has higher efficiency of utilization of photodiode array than that based on SIS. (3) 3D image based on SVS has the properties of rotation and scaling invariance. (4) The system based on SVS has higher echo power in outside ring of large photodiode array, which is more effective in detecting targets with low reflectance. [ABSTRACT FROM AUTHOR]

In this paper a new approach called evolving principal component clustering is applied to a data stream. Regions of the data described by linear models are identified. The method recursively estimates the data variance and the linear model parameters for each cluster of data. It enables good performance, robust operation, low computational complexity and simple implementation on embedded computers. The proposed approach is demonstrated on real and simulated examples from laser-range-finder data measurements. The performance, complexity and robustness are validated through a comparison with the popular split-and-merge algorithm. [ABSTRACT FROM AUTHOR]

Service robots are not only expected to navigate within the environment, as they also will may with people. Human tracking by mobile robots is essential for service robots and human interaction applications. In this work, the goal is to add a more natural robot-human following in front based on the normal human gait model. This approach proposes implementing and evaluating a human-robot interaction strategy, using the integration of a LRF (Laser Range Finder) tracking of human legs with wearable IMU (Inertial Measurement Unit) sensors for capturing the human movement during the gait. The work was carried out in four stages: first, the definition of the model of human-robot interaction and the control proposal were developed. Second, the parameters based on the human gait were estimated. Third, the robot and sensor integration setup are also proposed. Finally, the description of the algorithm for parameters detection is presented. In the experimental study, despite of the continuous oscillation during the walking, the parameters estimation was precise and unbiased, showing also repeatability with human linear velocities changes. The controller was evaluated with an eight-shaped curve, showing the stability of the controller even with sharp changes in the human path during real experiments. [ABSTRACT FROM AUTHOR]

Abstract: In order to realize high range accuracy, two laser range finder systems using broadband femtosecond lasers as optical sources are discussed and compared in detail. One system uses a traditional cross correlation method, and the other system uses a frequency resolved dispersion compensation method. Better than 0.65μm distance accuracy and ambiguity range of pulse-to-pulse separation can be realized in both systems. In the system using the traditional cross correlation method, submicron step resolution of the scanning system should be maintained to yield an authentic cross correlation graph. While in the system using the frequency resolved dispersion compensation method, no scanning system is required as the signal is spectrally resolved into several channels and the process is finished in software. In the system using the frequency resolved dispersion compensation method, the influence of channel number is much more significant than that of channel spacing and individual channel bandwidth. And the channel spacing should be a multiple integral of the laser repetition rate to facilitate the measurement; otherwise the optical path difference must be controlled to less than a pulse-to-pulse separation. Both systems are well suited to the range lidar requiring submicron distance accuracy, and can be applied in the future space missions. [Copyright &y& Elsevier]

Highlights: [•] An autonomous navigation algorithm based on a Particle Filter (PF) was developed. [•] The robot uses only a Laser Range finder sensing the environment. [•] A new probabilistic LIDAR model is developed to be used as measurement model in PF. [•] Robustness demonstrated with field tests in to different field conditions. [ABSTRACT FROM AUTHOR]

Abstract: This work proposes a system for long-term mapping and localization based on the Feature Stability Histogram (FSH) model which is an innovative feature management approach able to cope with changing environments. FSH is built using a voting schema, where re-observed features are promoted; otherwise the feature progressively decreases its corresponding FSH value. FSH is inspired by the human memory model. This model introduces concepts of Short-Term Memory (STM), which retains information long enough to use it, and Long-Term Memory (LTM), which retains information for longer periods of time. If the entries in STM are continuously rehearsed, they become part of LTM. However, this work proposes a change in the pipeline of this model, allowing any feature to be part of STM or LTM depending on the feature strength. FSH stores the stability values of local features, stable features are only used for localization and mapping. Experimental validation of the FSH model was conducted using the FastSLAM framework and a long-term dataset collected during a period of one year at different environmental conditions. The experiments carried out include qualitative and quantitative results such as: filtering out dynamic objects, increasing map accuracy, scalability, and reducing the data association effort in long-term runs. [Copyright &y& Elsevier]

Abstract: In this paper, a novel extrinsic calibration method of a camera and a laser range finder is presented using a calibration cube. The calibration is based on observing three edges of the cube from the camera and three laser points projected onto the edges from the laser finder. The equation for extrinsic calibration parameters, a rotation matrix and a translation vector, is built based on the constraint that in the image coordinate the projection of laser point onto the image plane is on that of the three cube edges. The parameters are calculated with three changes of pose of the cube. Experimental result shows that the proposed method is feasible and accurate. [Copyright &y& Elsevier]

Abstract: A new range equation for laser range finder using Gaussian beam profile is proposed. Unlike conventional range equations, the proposed equation can be applied to any ranging conditions, regardless of a beam size of incident light upon targets. We also show an operational coefficient for estimating the returned power using the proposed Gaussian beam range equation as an empirical correction factor. [Copyright &y& Elsevier]

Abstract: This paper describes a novel registration approach that is based on a combination of visual and 3D range information. To identify correspondences, local visual features obtained from images of a standard color camera are compared and the depth of matching features (and their position covariance) is determined from the range measurements of a 3D laser scanner. The matched depth-interpolated image features allow one to apply registration with known correspondences. We compare several ICP variants in this paper and suggest an extension that considers the spatial distance between matching features to eliminate false correspondences. Experimental results are presented in both outdoor and indoor environments. In addition to pair-wise registration, we also propose a global registration method that registers all scan poses simultaneously. [Copyright &y& Elsevier]