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Lidar technology has been widely used in both robotics and geomatics for environment perception and mapping. Moving object detection is important in both fields as it is a fundamental step for collision avoidance, static background extraction, moving pattern analysis, etc. A simple method involves checking directly the distance between nearest points from the compared datasets. However, large distances may be obtained when two datasets have different coverages. The use of occupancy grids is a popular approach to overcome this problem. There are two common theories employed to model occupancy and to interpret the measurements, Dempster-Shafer theory and probability. This paper presents a comparative study of these two theories for occupancy modelling with the aim of moving object detection from lidar point clouds. Occupancy is modelled using both approaches and their implementations are explained and compared in details. Two lidar datasets are tested to illustrate the moving object detection results. [ABSTRACT FROM AUTHOR]

This paper presents a mapping system that is suitable for small mobile robots. An ad hoc algorithm for mapping based on the Occupancy Grid method has been developed. The algorithm includes some simplifications in order to be used with low-cost hardware resources. The proposed mapping system has been built in order to be completely autonomous and unassisted. The proposal has been tested with a mobile robot that uses infrared sensors to measure distances to obstacles and uses an ultrasonic beacon system for localization, besides wheel encoders. Finally, experimental results are presented.

Computing Occupancy grids with LiDAR data, is a popular strategy for environment representation. In the last two decades, several authors have proposed different methods to render the sensed information into the grids, seeking to obtain computational efficiency or accurate environment modeling. However, no comparison regarding their performance under object detection in autonomous driving applications has been found in the literature. As a result, this work compares six representative LiDAR scan rendering strategies in a quantitative manner. To that end, a novel quantitative evaluation framework for occupancy grids is proposed. It addresses the two main steps of object detection: object segmentation and features estimation, proposing a meaningful procedure, repeatable with other OG approaches. The code of this evaluation framework is available in https://git-autopia.car.upm-csic.es/open_source/occupancy_grid_object_detection_evaluation.git. • New evaluation method for occupancy grids. • Evaluation is performed from an object detection autonomous driving perspective. • Object detection metrics are adapted for their application in occupancy grids. • Six methods for rendering LiDAR data into occupancy grids are compared. [ABSTRACT FROM AUTHOR]

An advanced sonar morphology-based posterior approach (SMP) to building an occupancy grid map for a mobile robot is proposed in this study. It is very important for a mobile robot to find its surrounding saliencies and to localize itself for indoor navigation. Ultrasonic sensors are of great practical value in building environmental maps and for autonomous operation. However, grid maps constructed by ultrasonic sensors cannot typically form a realistic representation of a given environment due to incorrect sonar measurements caused by specular reflection and wide beam width. The sonar sensor model proposed in this study, in which the negative effect of incorrect sonar measurements is minimized by geometric association with sonar footprints, is adopted to build a high-quality grid map. Experimental results and evaluations in home and corridor environments demonstrate the validity of the proposed methods. [ABSTRACT FROM AUTHOR]

The driving environment is complex and dynamic, and the attention of the driver is continuously challenged, therefore computer based assistance achieved by processing image and sensor data may increase traffic safety. While active sensors and stereovision have the advantage of obtaining 3D data directly, monocular vision is easy to set up, and can benefit from the increasing computational power of smart mobile devices, and from the fact that almost all of them come with an embedded camera. Several driving assistance application are available for mobile devices, but they are mostly targeted for simple scenarios and a limited range of obstacle shapes and poses. This paper presents a technique for generic, shape independent real-time obstacle detection for mobile devices, based on a dynamic, free form 3D representation of the environment: the particle based occupancy grid. Images acquired in real time from the smart mobile device's camera are processed by removing the perspective effect and segmenting the resulted bird-eye view image to identify candidate obstacle areas, which are then used to update the occupancy grid. The occupancy grid tracked cells are grouped into obstacles depicted as cuboids having position, size, orientation and speed. The easy to set up system is able to reliably detect most obstacles in urban traffic, and its measurement accuracy is comparable to a stereovision system. [ABSTRACT FROM AUTHOR]

In order for robotics to be used in automated production facilities, the manufacturing systems must be highly constrained. Despite the efforts of designers to generate accurate simulations, differences often occur in the real implementation. These differences are especially common in flexible manufacturing where people are required to operate in the same space as automated machines. Objects located in unexpected positions or orientations, additional objects in the area or missing objects can cause a robot to perform the designated operations incorrectly leading to wastage and loss of production. Typically, robotic systems have no information about the real environment around it and operate blind. In order to provide more information to the robot about its environment, information such as 3D scans can be provided to the system. This research compares a 3D scan of what is present in the environment to a 3D simulation of what is expected in the environment. The simulation scan comparison (SSC) process detects missing, unknown and incorrectly located objects. The results from the comparison process can be used to make decisions about how operations should be performed so that the outcome is satisfactory. For example, if an unknown object is in the path of a robot, the robot can be rerouted around the object. The surfaces of both simulation objects and 3D scan data are described using triangles, allowing triangle neighbour data to be used for more efficient identification of unknown objects, and triangle normal data to detect missing objects. In addition, occupancy grids have been employed for both the path planning and surface-matching processes further increasing the computational efficiency of the system. The SSC process was used to detect missing, unknown and incorrectly located objects in a typical robot cell to an accuracy of 98 %, thus demonstrating that the SSC process is capable of identifying unknown objects or unexpected situations and using these detections to update the original simulation or alter the manufacturing process. [ABSTRACT FROM AUTHOR]

This paper presents a mapping system that is suitable for small mobile robots. An ad hoc algorithm for mapping based on the Occupancy Grid method has been developed. The algorithm includes some simplifications in order to be used with low-cost hardware resources. The proposed mapping system has been built in order to be completely autonomous and unassisted. The proposal has been tested with a mobile robot that uses infrared sensors to measure distances to obstacles and uses an ultrasonic beacon system for localization, besides wheel encoders. Finally, experimental results are presented. [ABSTRACT FROM AUTHOR]

In this paper, the problem of goal seeking by robots in unknown environments is considered. A frontier based algorithm is proposed for finding a route to a goal in a fully unknown environment, where only the information about the goal region (GR), that is the region where the goal is most likely to be located, is available. The paper uses the concept of frontier cells, which are on the boundary between explored space and unexplored space. A 'goal seeking index' is defined for each frontier cell and used to choose the best among them. Modification of the algorithm is proposed with altered choice of frontier cells when wall like obstacles are encountered or when the robot falls in a 'trap' situation, to further reduce the number of moves toward the goal. The algorithm is tested extensively in computer simulations as well as in experiments and the results demonstrate that the algorithm effectively directs the robot to the goal and completes the search task in minimal number of moves. The solution to the problem of local minimum is also addressed, which helps in easy escape from a dead-end or dead-lock situation. It is shown that the proposed algorithm performs better than the state of the art agent centered search algorithm RTAA*. [ABSTRACT FROM AUTHOR]

Abstract: A map building algorithm for mobile robots is introduced in this paper. The perceived environment is represented in a map containing in each cell a probability of presence of an object or part of an object. The environment is represented as a collection of modular occupancy grids which are added to the map as far as the mobile robot finds objects outside the existing grids. In this approach a time-of-flight (ToF) camera is exploited as a range sensor for mapping. Indeed, one of the areas where ToF sensors are adequate is in obstacle avoidance, because the detection region is not only horizontal but also vertical, allowing to detect obstacles with complex shapes. The main steps of the map building algorithm are extensively described in the paper. The results of testing the algorithm are considered in two different indoor environments. [Copyright &y& Elsevier]

This paper deals with environment perception for automobile applications. Environment perception comprises measuring the surrounding field with onboard sensors such as cameras, radar, lidars, etc., and signal processing to extract relevant information for the planned safety or assistance function. Relevant information is primarily supplied using two well-known methods, namely, object based and grid based. In the introduction, we discuss the advantages and disadvantages of the two methods and subsequently present an approach that combines the two methods to achieve better results. The first part outlines how measurements from stereo sensors can be mapped onto an occupancy grid using an appropriate inverse sensor model. We employ the Dempster–Shafer theory to describe the occupancy grid, which has certain advantages over Bayes' theorem. Furthermore, we generate clusters of grid cells that potentially belong to separate obstacles in the field. These clusters serve as input for an object-tracking framework implemented with an interacting multiple-model estimator. Thereby, moving objects in the field can be identified, and this, in turn, helps update the occupancy grid more effectively. The first experimental results are illustrated, and the next possible research intentions are also discussed. [ABSTRACT FROM PUBLISHER]

Abstract: We present a real-time algorithm for simultaneous localization and local mapping (local SLAM) with detection and tracking of moving objects (DATMO) in dynamic outdoor environments from a moving vehicle equipped with a laser scanner, short-range radars and odometry. To correct the vehicle odometry we introduce a new fast implementation of incremental scan matching method that can work reliably in dynamic outdoor environments. After obtaining a good vehicle localization, the map surrounding of the vehicle is updated incrementally and moving objects are detected without a priori knowledge of the targets. Detected moving objects are finally tracked by a Multiple Hypothesis Tracker (MHT) coupled with an adaptive Interacting Multiple Model (IMM) filter. The experimental results on datasets collected from different scenarios such as: urban streets, country roads and highways demonstrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper describes and compares three different approaches to estimate simultaneous localization and mapping (SLAM) in dynamic outdoor environments. SLAM has been intensively researched in recent years in the field of robotics and intelligent vehicles, many approaches have been proposed including occupancy grid mapping method (Bayesian, Dempster-Shafer and Fuzzy Logic), Localization estimation method (edge or point features based direct scan matching techniques, probabilistic likelihood, EKF, particle filter). In this paper, a number of promising approaches and recent developments in this literature have been reviewed firstly in this paper. However, SLAM estimation in dynamic outdoor environments has been a difficult task since numerous moving objects exist which may cause bias in feature selection problem. In this paper, we proposed a possibilistic SLAM with RANSAC approach and implemented with three different matching algorithms. Real outdoor experimental result shows the effectiveness and efficiency of our approach. [ABSTRACT FROM AUTHOR]

This paper presents a novel strategy to develop a sensor model based on a probabilistic approach that would accurately provide information about individual sensor's uncertainties and limitations. The strategy also establishes the dependence of sensor's uncertainties on some of the environmental parameters or parameters of any feature extraction algorithm used in estimation based on scorner's outputs. To establish this dependence, the approach makes use of a neural network (NN) that is trained via an innovative technique that obtains training signal from a maximum likelihood (ML) estimator. The proposed technique was applied for modelling stereo-vision sensors and an infrared (IR) proximity sensor used in the robotic workcell available in the Robotics and Manufacturing Automation (RAMA) Laboratory at Duke University. In addition, the paper presents an innovative method to fuse the probabilistic information obtained from these sensors based on Bayesian formalism in an occupancy grid framework to obtain a three-dimensional model of the robotic workspace. The capability of the proposed technique in accurately obtaining three-dimensional occupancy profile and efficiently reducing individual sensor uncertainties was validated and compared with other methods via experiments carried out in the RAMA Laboratory. [ABSTRACT FROM AUTHOR]

The classical occupancy grid formulation requires the use of a priori known measurement likelihoods whose values are typically either assumed or learned from training data. Furthermore, in previous approaches, the likelihoods used to propagate the occupancy map variables are, in fact, independent of the state of interest and are derived from the spatial uncertainty of the detected point. This allows the use of a discrete Bayes filter as a solution to the problem, as discrete occupancy measurement likelihoods are used. In this paper, we first shown that once the measurement space is redefined, theoretically accurate and state-dependant measurement likelihoods can be obtained and used in the propagation of the occupancy random variable. The required measurement likelihoods for occupancy filtering are, in fact, those commonly encountered in both the landmark detection and data association hypotheses decisions. However, the required likelihoods are generally a priori unknown as they are a highly non-linear function of the landmark's signal-to-noise ratio and the surrounding environment. The probabilistic occupancy mapping problem is therefore reformulated as a continuous joint estimation problem where the measurement likelihoods are treated as continuous random states which must be jointly estimated with the map. In particular, this work explicitly considers the sensors detection and false-alarm probabilities in the occupancy mapping formulation. A particle solution is proposed which recursively estimates both the posterior on the map and the measurement likelihoods. The ideas presented in this paper are demonstrated in the field robotics domain using a millimeter wave radar sensor and comparisons with previous approaches, using constant discrete measurement likelihoods, are shown. A manually constructed ground-truth map and satellite imagery are also provided for map validation. [ABSTRACT FROM AUTHOR]

This paper discusses the importance, the complexity and the challenges of mapping mobile robot's unknown and dynamic environment, besides the role of sensors and the problems inherited in map building. These issues remain largely an open research problems in developing dynamic navigation systems for mobile robots. The paper presents the state of the art in map building and localization for mobile robots navigating within unknown environment, and then introduces a solution for the complex problem of autonomous map building and maintenance method with focus on developing an incremental grid based mapping technique that is suitable for real-time obstacle detection and avoidance. In this case, the navigation of mobile robots can be treated as a problem of tracking geometric features that occur naturally in the environment of the robot. The robot maps its environment incrementally using the concept of occupancy grids and the fusion of multiple ultrasonic sensory information while wandering in it and stay away from all obstacles. To ensure real-time operation with limited resources, as well as to promote extensibility, the mapping and obstacle avoidance modules are deployed in parallel and distributed framework. Simulation based experiments has been conducted and illustrated to show the validity of the developed mapping and obstacle avoidance approach. [ABSTRACT FROM AUTHOR]

In this article, we describe an algorithm for acquiring occupancy grid maps with mobile robots. The standard occupancy grid mapping developed by Elfes and Moravec in the mid-1980s decomposes the high-dimensional mapping problem into many one-dimensional estimation problems, which are then tackled independently. Because of the independencies between neighboring grid cells, this often generates maps that are inconsistent with the sensor data. To overcome this, we propose a cluster that is a set of cells. The cells in the clusters are tackled dependently with another occupancy grid mapping with an expectation maximization (EM) algorithm. The occupancy grid mapping with an EM algorithm yields more consistent maps, especially in the cluster. As we use the mapping algorithm adaptively with clusters according to the sensor measurements, our mapping algorithm is faster and more accurate than previous mapping algorithms. [ABSTRACT FROM AUTHOR]

Reliable and efficient perception and reasoning in dynamic and densely cluttered environments are still major challenges for driver assistance systems. Most of today's systems use target tracking algorithms based on object models. They work quite well in simple environments such as freeways, where few potential obstacles have to be considered. However, these approaches usually fail in more complex environments featuring a large variety of potential obstacles, as is usually the case in urban driving situations. In this paper we propose a new approach for robust perception and risk assessment in highly dynamic environments. This approach is called Bayesian occupancy filtering; it basically combines a four-dimensional occupancy grid representation of the obstacle state space with Bayesian filtering techniques. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Several problems can be encountered in the design of autonomous vehicles. Their software is organized into three main layers: perception, planning, and actuation. The planning layer deals with the sort and long-term situation prediction, which are crucial for intelligent vehicles. Whatever method is used to make forecasts, vehicles' dynamic environment must be processed for accurate long-term forecasting. In the present article, a method is proposed to preprocess the dynamic environment in a freeway traffic situation. The method uses the structured data of surrounding vehicles and transforms it to an occupancy grid which a Convolutional Variational Autoencoder (CVAE) processes. The grids (2048 pixels) are compressed to a 64-dimensional latent vector by the encoder and reconstructed by the decoder. The output pixel intensities are interpreted as probabilities of the corresponding field is occupied by a vehicle. This method's benefit is to preprocess the structured data of the dynamic environment and represent it in a lower-dimensional vector that can be used in any further tasks built on it. This representation is not handmade or heuristic but extracted from the database patterns in an unsupervised way. [ABSTRACT FROM AUTHOR]