Occupancy Map Research Articles

Drivable Region Estimation for Self-Driving Vehicles Using Radar

Self-driving vehicles are posing new challenges as the automation level defined in the SAE International standards for autonomous driving is increased. A pivotal task in autonomous driving is building a perception of the surrounding environment using optical sensors, which is a long-standing challenge and prompts us to explore the utilization of various sensors. Radar is an older and cheaper type of sensor than alternatives such as lidar for long-range distance coverage, and it is also competitively reliable and robust in adverse weather conditions. However, sparse data and noise are inherent challenges of radar. This study explores the dynamic Gaussian process for occupancy mapping and predicting a drivable path for a self-driving vehicle within the field of view (FOV) of a radar sensor. Gaussian occupancy mapping does not need abundant data for training and is a promising alternative to data-reliant deep learning techniques. The proposed technique optimizes parameters (variational and kernel-based) of the Gaussian process to determine the allowed region within the FOV limits by means of stochastic selection of functional points (pseudoinput) and tuning of threshold values. We have tested the proposed technique in experiments performed under different environmental conditions, such as various road and traffic conditions and diverse weather and illumination conditions. The results verify the efficacy of the proposed technique in diverse weather conditions for finding a drivable path for a self-driving vehicle, with the additional benefits of requiring only a low-cost apparatus and providing coverage of a long distance range.

3D Navigation Control of Untethered Magnetic Microrobot in Centimeter-Scale Workspace Based on Field-of-View Tracking Scheme

Automatic 3D navigation control of microrobot in large microenvironment is one of the primary challenges hindering its applications. In this article, we present a systematic approach to use an electromagnetic manipulation system to deal with this challenge. Two movable orthogonal microscopic cameras scan the microenvironment on the top view and side view to build a stereo occupancy map automatically. The initial position of microrobot can be located on the basis of the similarity curves generated by the scanning sequences. In accordance with the distribution characteristic of electromagnetic fields in workspace, an enhanced rapidly exploring random tree algorithm is proposed to avoid obstacles in the complex environment. To ensure continuous visual servo in wide range, a field-of-view tracking method is developed using a specific image definition evaluation algorithm. A prescribed performance controller with disturbance observer is designed, which guarantees that the microrobot can remain in the view of microscopic cameras, and the transient and steady-state performance of the system can satisfy the expected requirements. A set of experiments are performed to verify the effectiveness of the proposed automatic 3D navigation strategy. Experimental results show that the microrobot can navigate automatically in a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{12}\,\times 10\,\times \text{10-mm}$</tex-math></inline-formula> microenvironment containing obstacles, and achieve reliable field-of-view tracking and path following at high speed.

Cost-Effective Mapping of Mobile Robot Based on the Fusion of UWB and Short-Range 2-D LiDAR

Environment mapping is an essential prerequisite for mobile robots to perform different tasks such as navigation and mission planning. With the availability of low-cost 2-D LiDARs, there are increasing applications of such 2-D LiDARs in industrial environments. However, environment mapping in an unknown and featureless environment with such low-cost 2-D LiDARs remains a challenge. The challenge mainly originates from the short range of LiDARs and complexities in performing scan matching in these environments. In order to resolve these shortcomings, we propose to fuse the ultrawideband (UWB) with 2-D LiDARs to improve the mapping quality of a mobile robot. The optimization-based approach is utilized for the fusion of UWB ranging information and odometry to first optimize the trajectory. Then, the LiDAR-based loop closures are incorporated to improve the accuracy of the trajectory estimation. Finally, the optimized trajectory is combined with the LiDAR scans to produce the occupancy map of the environment. The performance of the proposed approach is evaluated in an indoor featureless environment with a size of 20 × 20 m. Obtained results show that the mapping error of the proposed scheme is 85.5% less than that of the conventional GMapping algorithm with short-range LiDAR (for example, Hokuyo URG-04LX in our experiment with a maximum range of 5.6 m).

Force-Based Simultaneous Mapping and Object Reconstruction for Robotic Manipulation

This letter presents an algorithm to simultaneously reconstruct the geometry of an unknown object and its environment via physical interactions. Applications involving highly cluttered or occluded workspaces prevent the effective use of vision. To address some of the challenges that arise, we propose an approach that instead utilizes force and torque measurements at the robot end-effector to solve for possible contact locations and probabilistically determine the occupancy likelihood on a 3D map. Our procedure constructs two occupancy maps: one fixed that represents the environment and another map that moves with the robot end-effector and reconstructs the grasped object shape, where each map informs the probability updates on the other. The algorithm is applied and tested on two scenarios: retrieving a tangled object from a scene and reconstructing the geometry of an object. We compare the results against a configuration space planner and a reinforcement learning algorithm, with our method requiring fewer collisions with the environment to extract the object.

A Unified Framework for Large-Scale Occupancy Mapping and Terrain Modeling Using RMM

Building suitable representations for diversified environments to enable robot autonomous navigation is a complicated task, especially for large-scale environments, where the captured vast amount of data will give rise to computation and storage bottlenecks. In this letter, we first propose the random mapping method (RMM), which can efficiently project the irregular points in the low-dimensional data set into the high-dimensional one, where the points are approximately linearly separable or distributed. In the mapped space, we then propose a unified environment modeling framework in the form of linear parametric model, which can represent the occupancy maps and terrain models consistently. Adopting the idea of parallel computing, we then apply our method to the large-scale environment modeling to reduce the wall-clock time of calculation without losing much accuracy. Experiments were fully conducted to evaluate the proposed random mapping method and the proposed environmental modeling method, showing their better comprehensive performance compared to the typical methods and state-of-the-art methods.

Australian parents' experiences of owning an autism assistance dog.

Autism assistance dogs (AADs) increase safety for children with autism and their families. Autism assistance dogs can also decrease familial stress and the isolation which families may experience due to fear for their child's safety and judgement from others within the community. Currently there is a paucity of literature on parents' experiences of AADs. Therefore, this study aimed to develop a rich understanding of parents' experiences of owning an AAD. A mixed methods design was utilised, with a qualitative descriptive design and the use of occupational mapping. Eight families were recruited through an Australian AAD programme and participated in semi-structured in-depth interviews throughout 2017. The interviews were analysed thematically. Mobility in the community before and after introduction of the dog was measured using occupational mapping. Families plotted on Google Map printouts the places they frequented before and after placement of their dog. Five major themes emerged from the analysis of the interviews: freedom through restraint; expanding our world; a calming/sensory tool (AAD); "at the end of the day they're dogs"; and, friendship and personal growth. The occupational maps demonstrated a median increase of 8.5 more places and 20.50km further travelled from home after having the dog for over a year. Families with an AAD experienced an expanded world for the child and their family. Families experienced freedom in the places they could go, decreased isolation due to the safety which the dog provides. Occupational mapping supported the qualitative data, showing increased mobility and decreased isolation of the family. The paradox of freedom through restraint is a new and key finding which requires further exploration. The results provide support for funding and increased awareness of AAD programmes. Future longitudinal comparative studies are needed to explore the long-term impact of AADs on the child and family.

Improving obstacle boundary representations in predictive occupancy mapping

Predictive, inference-based occupancy mapping has been used successfully in many instances to create accurate and descriptive maps from sparse data, defining occupied space in a manner suitable to support autonomous navigation. However, one key drawback of inferring occupancy based largely on the proximity of range sensor observations is inaccuracy at the boundary between occupied and free space, where sparse coverage by the sensor data can be misinterpreted. To obtain a more accurate representation of the boundary between free and occupied space, we propose several modifications to a recently published occupancy mapping algorithm that uses Bayesian generalized kernel inference. In particular, our proposed algorithm distinguishes between unknown map cells with insufficient observations, and those which are uncertain due to disagreement among numerous observations, in a predictive, inference-based occupancy map. This distinction is key to our improved ability to capture ambiguities arising at the boundary between free and occupied space. We validate our approach using synthetic range data from a simulated environment and demonstrate real-time mapping performance using range data acquired by a ground robot operating in an underground mine.

CASowary: CRISPR-Cas13 guide RNA predictor for transcript depletion

BackgroundRecent discovery of the gene editing system - CRISPR (Clustered Regularly Interspersed Short Palindromic Repeats) associated proteins (Cas), has resulted in its widespread use for improved understanding of a variety of biological systems. Cas13, a lesser studied Cas protein, has been repurposed to allow for efficient and precise editing of RNA molecules. The Cas13 system utilizes base complementarity between a crRNA/sgRNA (crispr RNA or single guide RNA) and a target RNA transcript, to preferentially bind to only the target transcript. Unlike targeting the upstream regulatory regions of protein coding genes on the genome, the transcriptome is significantly more redundant, leading to many transcripts having wide stretches of identical nucleotide sequences. Transcripts also exhibit complex three-dimensional structures and interact with an array of RBPs (RNA Binding Proteins), both of which may impact the effectiveness of transcript depletion of target sequences. However, our understanding of the features and corresponding methods which can predict whether a specific sgRNA will effectively knockdown a transcript is very limited.ResultsHere we present a novel machine learning and computational tool, CASowary, to predict the efficacy of a sgRNA. We used publicly available RNA knockdown data from Cas13 characterization experiments for 555 sgRNAs targeting the transcriptome in HEK293 cells, in conjunction with transcriptome-wide protein occupancy information. Our model utilizes a Decision Tree architecture with a set of 112 sequence and target availability features, to classify sgRNA efficacy into one of four classes, based upon expected level of target transcript knockdown. After accounting for noise in the training data set, the noise-normalized accuracy exceeds 70%. Additionally, highly effective sgRNA predictions have been experimentally validated using an independent RNA targeting Cas system – CIRTS, confirming the robustness and reproducibility of our model’s sgRNA predictions. Utilizing transcriptome wide protein occupancy map generated using POP-seq in HeLa cells against publicly available protein-RNA interaction map in Hek293 cells, we show that CASowary can predict high quality guides for numerous transcripts in a cell line specific manner.ConclusionsApplication of CASowary to whole transcriptomes should enable rapid deployment of CRISPR/Cas13 systems, facilitating the development of therapeutic interventions linked with aberrations in RNA regulatory processes.

Global and context-specific transcriptional consequences of oncogenic Fbw7 mutations.

The Fbw7 ubiquitin ligase targets many proteins for proteasomal degradation, which include oncogenic transcription factors (TFs) (e.g., c-Myc, c-Jun, and Notch). Fbw7 is a tumor suppressor and tumors often contain mutations in FBXW7, the gene that encodes Fbw7. The complexity of its substrate network has obscured the mechanisms of Fbw7-associated tumorigenesis, yet this understanding is needed for developing therapies. We used an integrated approach employing RNA-Seq and high-resolution mapping (cleavage under target and release using nuclease) of histone modifications and TF occupancy (c-Jun and c-Myc) to examine the combinatorial effects of misregulated Fbw7 substrates in colorectal cancer (CRC) cells with engineered tumor-associated FBXW7 null or missense mutations. Both Fbw7 mutations caused widespread transcriptional changes associated with active chromatin and altered TF occupancy: some were common to both Fbw7 mutant cell lines, whereas others were mutation specific. We identified loci where both Jun and Myc were coregulated by Fbw7, suggesting that substrates may have synergistic effects. One coregulated gene was CIITA, the master regulator of MHC Class II gene expression. Fbw7 loss increased MHC Class II expression and Fbw7 mutations were correlated with increased CIITA expression in TCGA colorectal tumors and cell lines, which may have immunotherapeutic implications for Fbw7-associated cancers. Analogous studies in neural stem cells in which FBXW7 had been acutely deleted closely mirrored the results in CRC cells. Gene set enrichment analyses revealed Fbw7-associated pathways that were conserved across both cell types that may reflect fundamental Fbw7 functions. These analyses provide a framework for understanding normal and neoplastic context-specific Fbw7 functions.

Love thy neighbour?—Spatial variation in density dependence of nest survival in relation to predator community

AbstractAimIn many species, density‐dependent effects on reproduction are an important driver of population dynamics. However, it is rarely considered that the direction of density dependence is expected to vary over space and time depending on anti‐predator behaviour and predator community. Aggregation may allow for effective group mobbing against avian nest predators while aggregation may also attract mammalian predators, causing negative density dependence. We aim to quantify spatial variation in the effect of conspecific breeding density on nest survival in a mobbing bird species (Eurasian oystercatcher;Haematopus ostralegus) and identify whether this variation in density dependence can be explained by the predator community.LocationCountry‐wide (The Netherlands).MethodsWe integrated reproductive data with breeding territory maps of Eurasian oystercatchers and occupancy maps of avian and mammalian predator species across the Netherlands for a 10‐year period.ResultsSpatial variation in the composition of the predator community explained the effects of neighbour density, showing decreasing nest survival when both conspecific density and mammalian dominance increased. Also, heterospecific density (from breeding godwits and lapwing) has an additional effect on the oystercatcher nest survival. Strikingly, this pattern did not extend to mammal‐free island populations.Main conclusionsOur study provides evidence that both the strength and sign of density dependence can vary spatially within species, implying that it is dangerous to generalize results from a single local population to large‐scale management implications and modelling exercises. The study also suggests that conservation actions that aim to attract breeding birds should be prioritized in areas with fewer mammalian predators, but this idea requires further testing on island populations.

Sensor‐driven autonomous underwater inspections: A receding‐horizon RRT‐based view planning solution for AUVs

AbstractAutonomous Underwater Vehicles (AUVs) are used by the scientific community for various applications, from collecting well‐distributed high‐quality data to mapping the seafloor or exploring unknown areas. Nonpredictable environmental conditions and sensor acquisitions make the design of AUV surveys challenging even for expert operators. Multiple attempts are required, and the collected data quality is not guaranteed: The AUV passively stores the sensors' acquisitions that are then analyzed offline after its recovery. In Forward‐Looking SONAR (FLS) seabed inspections, the vehicle follows lawnmower paths designed by an expert operator that considers the sensor characteristics. The performance of FLSs is affected by several environmental conditions and possible protruding objects. This paper presents a probabilistic framework for FLS‐based seabed inspections that endow the AUV with the ability to autonomously conducting the survey and ensure adequate coverage of the target area. A three‐dimensional probabilistic occupancy mapping system for FLS reconstructions to update the covered area map was developed. The map is used by the Coverage Path Planning (CPP) algorithm to evaluate the visibility of the viewpoints that are generated as nodes of a random tree. The Next‐Best Viewpoint (NBV) is selected as the first node in the branch expected to collect more data, and the path to reach the NBV is computed using the rapidly exploring random tree (RRT*) algorithm. The sensor‐driven coverage approach is used in a receding‐horizon manner. The proposed Receding‐Horizon Coverage Approach was validated with simulations and real prerecorded data. Finally, the framework was used online during an experimental campaign where several FLS seabed inspections were performed.

Solving the Datum Search as a Partially Observed Stochastic Game

The Flaming Datum (FD) problem refers to the search for a hostile Evader that is fleeing after momentarily revealing its position. If Evader&#x2019;s direction is fixed but unknown, while its maximal speed is known, Koopman argued in 1980 that the best trajectory for Searcher is a spiral starting from the revealed position. The objective of our study is to verify the hypothesis of a spiral search path, by formulating the FD problem in the framework of a finite two-player zero-sum partially observed stochastic search game, where the opponent plays repeatedly a fixed but unknown pure strategy. Using a realistic sensor model, current information about the position of Evader is represented by an occupancy map, updated in the Bayesian framework. The utility is computed as the entropy reduction of the occupancy map. The game was implemented in software and solved using the maxmin method. By running repeatedly the described search game, we have found that the search pattern, although random (due to the uncertainties in sensing and mixed strategies), is indeed a spiral on every play of the game, thus confirming the hypothesis.

Mapeamento dos serviços ecossistêmicos de Áreas de Preservação Permanente de cursos d’água: estudo de caso da bacia do rio Doce

The Permanent Preservation Area (PPA) is an area protected by law with the function of preserving environmental resources, especially water resources, in addition to ensuring human well-being. They promote the provision of fundamental ecosystem services for the conservation of life through the maintenance of ecological cycles based on the ecosystem functions of support, regulation, provision, and culture. Justified by the importance and the particular characteristics of the PPAs, a classification of ecosystem services was carried out specifically for these environments, attributing a weight scale with values from “1” to “5” to the capacity to offer ecosystem services. From the mapping of land use and occupation, the weights of APP services were associated with land cover, resulting in the mapping of ecosystem services in APPs in the Doce river basin. The mapping of areas with higher density of high offer of ecosystem services allowed the spatial identification of the offer of ecosystem services for the maintenance, conservation and regeneration of ecosystems. In this way, the inadequate land-use of APPs in the Rio Doce basin was related to the loss of ecosystem services together with ecological, economic and social impacts, highlighting the relevance of maintaining watercourses and aiming at the preservation and application of resources of these areas to the recovery of degraded regions such as those impacted by the rupture of the Barragem do Fundão, in the municipality of Mariana

Dynamic Semantic Occupancy Mapping Using 3D Scene Flow and Closed-Form Bayesian Inference

This paper reports on a dynamic semantic mapping framework that incorporates 3D scene flow measurements into a closed-form Bayesian inference model. Existence of dynamic objects in the environment can cause artifacts and traces in current mapping algorithms, leading to an inconsistent map posterior. We leverage state-of-the-art semantic segmentation and 3D flow estimation using deep learning to provide measurements for map inference. We develop a Bayesian model that propagates the scene with flow and infers a 3D continuous (i.e., can be queried at arbitrary resolution) semantic occupancy map outperforming its static counterpart. Extensive experiments using publicly available data sets show that the proposed framework improves over its predecessors and input measurements from deep neural networks consistently.

ChIP-Seq Occupancy Mapping of the Archaeal Transcription Machinery.

Genome-wide occupancy studies for RNA polymerases and their basal transcription factors deliver information about transcription dynamics and the recruitment of transcription elongation and termination factors in eukaryotes and prokaryotes. The primary method to determine genome-wide occupancies is chromatin immunoprecipitation combined with deep sequencing (ChIP-seq). Archaea possess a transcription machinery that is evolutionarily closer related to its eukaryotic counterpart but it operates in a prokaryotic cellular context. Studies on archaeal transcription brought insight into the evolution of transcription machineries and the universality of transcription mechanisms. Because of the limited resolution of ChIP-seq, the close spacing of promoters and transcription units found in archaeal genomes pose a challenge for ChIP-seq and the ensuing data analysis. The extreme growth temperature of many established archaeal model organisms necessitates further adaptations. This chapter describes a version of ChIP-seq adapted for the basal transcription machinery of thermophilic archaea and some modifications to the data analysis.

Sensor Data Fusion for a Mobile Robot Using Neural Networks.

Mobile robots must be capable to obtain an accurate map of their surroundings to move within it. To detect different materials that might be undetectable to one sensor but not others it is necessary to construct at least a two-sensor fusion scheme. With this, it is possible to generate a 2D occupancy map in which glass obstacles are identified. An artificial neural network is used to fuse data from a tri-sensor (RealSense Stereo camera, 2D LiDAR, and Ultrasonic Sensors) setup capable of detecting glass and other materials typically found in indoor environments that may or may not be visible to traditional 2D LiDAR sensors, hence the expression improved LiDAR. A preprocessing scheme is implemented to filter all the outliers, project a 3D pointcloud to a 2D plane and adjust distance data. With a Neural Network as a data fusion algorithm, we integrate all the information into a single, more accurate distance-to-obstacle reading to finally generate a 2D Occupancy Grid Map (OGM) that considers all sensors information. The Robotis Turtlebot3 Waffle Pi robot is used as the experimental platform to conduct experiments given the different fusion strategies. Test results show that with such a fusion algorithm, it is possible to detect glass and other obstacles with an estimated root-mean-square error (RMSE) of 3 cm with multiple fusion strategies.

Occupancy grid mapping for rover navigation based on semantic segmentation

Obstacle mapping is a fundamental building block of the autonomous navigation pipeline of many robotic platforms such as planetary rovers. Nowadays, occupancy grid mapping is a widely used tool for obstacle perception. It foreseen the representation of the environment in evenly spaced cells, whose posterior probability of being occupied is updated based on range sensors measurement. In more classic approaches, the cells are updated to occupied at the point where the ray emitted by the range sensor encounters an obstacle, such as a wall. The main limitation of this kind of methods is that they are not able to identify planar obstacles, such as slippery, sandy, or rocky soils. In this work, we use the measurements of a stereo camera combined with a pixel labeling technique based on Convolution Neural Networks to identify the presence of rocky obstacles in planetary environment. Once identified, the obstacles are converted into a scan-like model. The estimation of the relative pose between successive frames is carried out using ORB-SLAM algorithm. The final step consists of updating the occupancy grid map using the Bayes’ update Rule. To evaluate the metrological performances of the proposed method images from the Martian analogous dataset, the ESA Katwijk Beach Planetary Rover Dataset have been used. The evaluation has been performed by comparing the generated occupancy map with a manually segmented ortomosaic map, obtained by drones’ survey of the area used as reference.

An Occupancy Mapping Method Based on K-Nearest Neighbours.

OctoMap is an efficient probabilistic mapping framework to build occupancy maps from point clouds, representing 3D environments with cubic nodes in the octree. However, the map update policy in OctoMap has limitations. All the nodes containing points will be assigned with the same probability regardless of the points being noise, and the probability of one such node can only be increased with a single measurement. In addition, potentially occupied nodes with points inside but traversed by rays cast from the sensor to endpoints will be marked as free. To overcome these limitations in OctoMap, the current work presents a mapping method using the context of neighbouring points to update nodes containing points, with occupancy information of a point represented by the average distance from a point to its k-Nearest Neighbours. A relationship between the distance and the change in probability is defined with the Cumulative Density Function of average distances, potentially decreasing the probability of a node despite points being present inside. Experiments are conducted on 20 data sets to compare the proposed method with OctoMap. Results show that our method can achieve up to 10% improvement over the optimal performance of OctoMap.

A Novel Occupancy Mapping Framework for Risk-Aware Path Planning in Unstructured Environments.

In the context of autonomous robots, one of the most important tasks is to prevent potential damage to the robot during navigation. For this purpose, it is often assumed that one must deal with known probabilistic obstacles, then compute the probability of collision with each obstacle. However, in complex scenarios or unstructured environments, it might be difficult to detect such obstacles. In these cases, a metric map is used, where each position stores the information of occupancy. The most common type of metric map is the Bayesian occupancy map. However, this type of map is not well suited for computing risk assessments for continuous paths due to its discrete nature. Hence, we introduce a novel type of map called the Lambda Field, which is specially designed for risk assessment. We first propose a way to compute such a map and the expectation of a generic risk over a path. Then, we demonstrate the benefits of our generic formulation with a use case defining the risk as the expected collision force over a path. Using this risk definition and the Lambda Field, we show that our framework is capable of doing classical path planning while having a physical-based metric. Furthermore, the Lambda Field gives a natural way to deal with unstructured environments, such as tall grass. Where standard environment representations would always generate trajectories going around such obstacles, our framework allows the robot to go through the grass while being aware of the risk taken.

Parameter Reduction and Optimisation for Point Cloud and Occupancy Mapping Algorithms.

Occupancy mapping is widely used to generate volumetric 3D environment models from point clouds, informing a robotic platform which parts of the environment are free and which are not. The selection of the parameters that govern the point cloud generation algorithms and mapping algorithms affects the process and the quality of the final map. Although previous studies have been reported in the literature on optimising major parameter configurations, research in the process to identify optimal parameter sets to achieve best occupancy mapping performance remains limited. The current work aims to fill this gap with a two-step principled methodology that first identifies the most significant parameters by conducting Neighbourhood Component Analysis on all parameters and then optimise those using grid search with the area under the Receiver Operating Characteristic curve. This study is conducted on 20 data sets with specially designed targets, providing precise ground truths for evaluation purposes. The methodology is tested on OctoMap with point clouds created by applying StereoSGBM on the images from a stereo camera. A clear indication can be seen that mapping parameters are more important than point cloud generation parameters. Moreover, up to 15% improvement in mapping performance can be achieved over default parameters.