Occupancy Grid Mapping with Cognitive Plausibility for Autonomous Driving Applications

<div class="section abstract"><div class="htmlview paragraph">In the domain of advanced driver assistance systems and autonomous vehicles, precise perception and interpretation of the vehicle's environment are not merely requirements they are the very foundation upon which every aspect of functionality and safety is constructed. One prevalent method of representing the environment is through the use of an occupancy grid map. This map segments the environment into distinct grid cells, each of which is evaluated to determine if it is occupied or free. This evaluation operates under the assumption that each grid cell is independent of the others. The underlying mathematical structure of this system is the binary Bayes filter (BBF). The BBF integrates sensor data from various sources and can incorporate measurements taken at different times. The occupancy grid map does not rely on the identification of individual objects, which allows it to depict obstacles of any shape. This flexibility is a key advantage of this approach. Traditional occupancy grid maps fall short when it comes to predicting dynamic environments due to their lack of a process model. A notable enhancement to this static model is the Bayesian Occupancy Filter (BOF), which, unlike its predecessor, estimates a velocity distribution for each grid cell's occupancy using a histogram filter. However, the BOF's computational demands are high. To address this, research propose representing the dynamic state of grid cells using particles. This method enables the computation of dynamic grid maps in real-time applications, even with larger grid cell sizes and higher resolution. Despite these advancements, dynamic occupancy grid maps remain a relatively new field of study, especially when compared to more established object-tracking approaches. Until now, the BOF has been treated as a distinct research area with minimal overlap with other tracking methodologies. This methodology aims to bridge that gap and foster a more integrated approach to dynamic environment estimation.</div><div class="htmlview paragraph">This study introduces a novel approach to dynamic grid mapping, conceptualized as an approximation of a Random Finite Set (RFS) filter. An RFS is a probabilistic representation of a finite, random collection of objects and their respective states. Finite Set Statistics (FISST) provide a framework for Bayesian filtering of these random finite sets and form the foundation for several multi-object tracking methodologies, such as the Probability Hypothesis Density (PHD) filter. By characterizing the grid as an RFS, we can apply sophisticated concepts from the well-established domain of RFS filtering to dynamic grid mapping. The research develops a filter known as the Probability Hypothesis Density/Multi-Instance Bernoulli (PHD/MIB) filter. This filter alternately represents and propagates the dynamic grid map as a PHD and as multiple instances of Bernoulli filters, thereby offering a more integrated and efficient approach to dynamic environment estimation.</div><div class="htmlview paragraph">Furthermore, this research introduces a Sequential Monte Carlo (SMC) implementation of the PHD/MIB filter, as well as an approximation within the Dempster-Shafer framework, termed the Dempster-Shafer PHD/MIB (DS-PHD/MIB) filter. This DS-PHD/MIB filter necessitates fewer particles than the original PHD/MIB filter, thereby enhancing computational efficiency. The study provides a comprehensive description of an efficient, massively parallel implementation of the DS-PHD/MIB filter. The algorithm's pseudo code is also outlined, offering a clear and concise understanding of its workings. This approach further strengthens the integrated and efficient methodology for dynamic environment estimation.</div><div class="htmlview paragraph">In conclusion, the research delineates the attributes of the DS-PHD/MIB filter and debates its pros and cons in comparison to object-based tracking methodologies, using practical examples for illustration. A quantitative assessment using real-world data demonstrates that the DS-PHD/MIB filter yields consistent state estimation outcomes. It effectively models both the stochastic multi-object transition process and the stochastic multi-object observation process. Moreover, the evaluation affirms the real-time capability of the parallelized implementation of the DS-PHD/MIB filter. It validates its utility for state estimation in dynamic vehicle environments, thereby underscoring its potential for practical applications in dynamic environment estimation. This comprehensive approach offers a promising avenue for future research and development in this field.</div></div>

This article is concerned with 2D indoor environment mapping produced by a sensor data fusion algorithm. A measurement subsystem assembled on a mobile robot uses its sensors to gather data from its surroundings. In this article, an algorithm is proposed and evaluated to fuse data acquired by the following low-cost sensors: a visual sensor (a wireless webcam with a laser pointer) and three range finder sensors (two infrared units and a sonar transducer). The main steps of the proposed solution are: (a) at each accurate robot pose an occupancy grid (OG) probabilistic map is generated for each type of sensor, (b) the three OG maps are merged using competitive fusion and the RANSAC algorithm is employed to extract line segments, (c) the line segments are further processed using competitive and/or complementary fusion resulting in a feature-based map for each robot pose. The previous steps are repeated until all robots pose are computed. The feature-based maps for all robot poses are then merged using competitive fusion, and a final precise OG map of the environment is generated. The proposed algorithm was evaluated using data gathered by the SLAMVITA robot sensors in two small test environments. The experimental results have shown that the proposed algorithm was able to build precise maps of the test environments (error less than 1.5 %).

Occupancy grid state mapping is a key process in robotics and autonomous driving systems. It divides the environment into grid cells that contain information states. In this paper, we propose a modified SMC-BOF method to map and predict occupancy grids. The original SMC-BOF has been widely used in the occupancy grid mapping because it has lower computational costs than the BOF method. However, there are some issues related to conflicting information in dynamic situations. The original SMC-BOF cannot completely control an elongated vehicle that has conflicting information caused by the height difference between backward of vehicle and ground. To overcome this problem, we add confidence weights onto a part of the grid mapping process of the original SMC-BOF using the Linear Opinion Pool. We evaluate our method by LIDAR and stereo vision data in the KITTI dataset.

The majority of planning algorithms used are based on the occupancy grid maps, but in complicated situations, the occupancy grid maps have a significant search overhead. This paper proposed a path planner based on the visibility graph (v-graph) for the mobile robot that uses sparse methods to speed up and simplify the construction of the v-graph. Firstly, the complementary grid framework is designed to reduce graph updating iteration costs during the data collection process in each data frame. Secondly, a filter approach based on the edge length and the number of vertices of the obstacle contour is proposed to reduce redundant nodes and edges in the v-graph. Thirdly, a bidirectional breadth-first search is combined into the path searching process in the proposed fast path planner algorithm in order to reduce the waste of exploring space. Finally, the simulation results indicate that the proposed sparse v-graph planner can significantly improve the efficiency of building the v-graph and reduce the time of path search. In highly convoluted unknown or partially known environments, our method is 40% faster than the FAR Planner and produces paths 25% shorter than it. Moreover, the physical experiment shows that the proposed path planner is faster than the FAR Planner in both the v-graph update process and laser process. The method proposed in this paper performs faster when seeking paths than the conventional method based on the occupancy grid.

In recent years, robotic swarms have been studied as an alternative to replace complex and expensive robots in the execution of different types of tasks. In this paper, we propose an approach to cooperatively build occupancy grid maps using large groups of simpler robots. Robots randomly explore the environment and update a shared occupancy grid stored in the cloud using Bayesian Filters. They localize themselves based on odometry and use pair-to-pair neighborhood communication to exchange pose information and reduce the uncertainty through a Kalman Filter. Simulated and real experiments are performed to show the effectiveness and scalability of the proposed approach.

We propose an occupancy grid mapping algorithm for mobile robots operating in environments where objects may change their locations over time. Most mapping algorithms rely on a static world assumption, which cannot model non-stationary objects (chairs, desks,...). This paper describes an extension to the well-known occupancy grid mapping technique [5,10] for learning models of non-stationary objects. Our approach uses a map differencing technique to extract snapshots of non-stationary objects. It then employs the expectation maximization (EM) algorithm to learn models of these objects, and to solve the data association problem that arises when objects are seen at different places at different points in time. A Bayesian version of Occam’s razor is applied to determine the number of different objects in the model. Experimental results obtained in two different indoor environments illustrate that our approach robustly solves the data association problem, and generates accurate models of environments with non-stationary objects.

We describe a new algorithm for occupancy grid mapping using LIDAR in the presence of glass and other non-diffuse surfaces. This is a major problem for robot navigation in many indoor environments due to the prevalence of glass paned doors, windows, and even glass walls, as well as mirrors and polished metal surfaces. Current formulations of occupancy grid mapping make the assumption that objects in the environment are detectable from all angles. However, glass and other specular surfaces are invisible to LIDAR at most angles and so become washed out as “noise”. We modify the standard occupancy grid algorithm to allow for mapping objects that are only visible from certain view angles, by tracking the subset of angles from which objects are reliably visible. We show that these angles can be determined reliably with a single pass through the environment, and that the information can be used to map both diffuse and specular surfaces.

This paper presents a motion prediction scheme of object vehicles based on the dynamic occupancy grid map considering movement of the vehicles by applying a temporal flow and a cascaded algorithm for support vector machine (SVM). We divided occupancy grid map into two types of upper-level and lower level. The upper-level occupancy grid is used to predict motion that the object vehicle can move into the ego vehicle and the lower-level one is needed for decision using the SVM for sensor resolution. The presented algorithm was validated with a experimental data set and the overall accuracy of classification was obtained 90.42% from a confusion matrix.

Rule‐based parking path planning algorithms have been widely employed in automated parking systems (APS), relying on the prior map or data collected by sensors to perceive the environment and generate paths. However, they often perform poorly in terms of perception and rapid path generation in complex scenes. In this paper, we propose the r oad s ide p arking p ath p lanning model (RSP3), a novel end‐to‐end model that combines the advantages of the roadside unit (RSU) and end‐to‐end neural networks. Based on the vehicle‐to‐infrastructure cooperation, the RSP3 can use the global view provided by the RSU to acquire the scene information and generate discrete path points on the occupancy grid map with a classification‐based modelling method. It reduces task complexity and enhances the abilities of learning and imitating the traditional algorithm with improved flexibility and robustness. We developed an automatic data collection and evaluation platform based on the CARLA simulator and used the hybrid A* algorithm as the baseline. The results show that the RSP3 can not only quickly perform accurate segmentation of the current scene and understand the intention of the autonomous driving vehicle but also generate high‐quality paths, which shows its potential in the efficient and robust automatic parking task. To advance further research, the code will be available at: https://github.com/ppllracing/RSP3.git .

Psychophysical sensitivity to isoluminant chromatic modulation declines at temporal frequencies beyond 4 Hz, whereas chromatically opponent cells of the afferent visual pathway (long- to middle-wavelength (L-M) cone-opponent or short-wavelength (S) cone cells) show responses at much higher temporal frequencies, indicating a central limitation in temporal processing capacity. Here, we sought to localize this limit in cortical retinotopic visual areas. We used fMRI to investigate responses of lateral geniculate nucleus and cortical visual areas in humans to isoluminant chromatic modulation as a function of temporal frequency (2-12 Hz). Our results suggest that L-M cone-opponent and S-cone signals are processed in LGN up to 12 Hz. In all visual areas except MT (middle temporal) and V3a, S-cone responses declined steeply with temporal frequency, implying that psychophysical sensitivity loss to blue-yellow modulation might occur early within these areas. While V1 showed robust L-M responses up to 12 Hz, there was a progressive falloff of responses with temporal frequency as information is transferred from V1 to higher areas (V2, V3, and V4), suggesting that, in humans, temporal limitation in perception of red-green chromatic modulation likely results from limited processing capacity of higher ventral extrastriate areas.

Occupancy grid mapping is an important approach for intelligent vehicle environment perception. In this paper, an occupancy grid mapping approach in Dezert-Smarandache theory (DSmT) framework for the purpose of dynamic environment perception is proposed. To avoid the transformation of the local map from polar to Catersian coordinate, a different inverse sensor model in Cartesian coordinate for laser scanner was proposed. Two different combination rules in DSmT framework, Dempster’s rule of combination and PCR2, are implemented independently for global map update and mobile object detection. The performance of the two combination rules were compared by ways of simulation and experiment. According to the comparisons we find that both of the combination rules are capable of detecting mobile objects. And the former effectively filtered out the noise and make the detection robust, but the latter didn’t, suggesting that the former is more suitable for occupancy grid mapping. Static and mobile objects are extracted from the occupancy grid map using digital image processing technology. Full Text: PDF DOI: http://dx.doi.org/10.11591/ijra.v2i4.923

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.

Human cognitive and perception processes have a great tolerance for imprecision or uncertainty. For this reason, the notions of perception and cognition have great importance in solving many decision making problems in engineering, medicine, science, and social science as there are innumerable uncertainties in real-world phenomena. These uncertainties can be broadly classified as either type one uncertainty arising from the random behavior of physical processes or type two uncertainty arising from human perception and cognition processes. Statistical theory can be used to model the former, but lacks the sophistication to process the latter. The theory of fuzzy logic has proven to be very effective in processing type two uncertainty. New computing methods based on fuzzy logic can lead to greater adaptability, tractability, robustness, a lower cost solution, and better rapport with reality in the development of intelligent systems. Fuzzy logic is needed to properly pose and answer queries about quantitatively defining imprecise linguistic terms like middle class, poor, low inflation, medium inflation, and high inflation. Imprecise terms like these in natural languages should be considered to have qualitative definitions, quantitative definitions, crisp quantitative definitions, fuzzy quantitative definitions, type-one fuzzy quantitative definitions, and interval type-two fuzzy quantitative definitions. There can be crisp queries, crisp answers, type-one fuzzy queries, type-one fuzzy answers, interval type-two fuzzy queries, and interval type-two fuzzy answers.

Occupancy grid maps are popular tools of representing surrounding environments for mobile robots/ intelligent vehicles. When moving in dynamic real world, traditional occupancy grid mapping is required not only to be able to detect occupied areas, but also to be able to understand the dynamic circumstance. The paper addresses this issue by presenting a stereo-vision based framework to create dynamic occupancy grid map, for the purpose of intelligent vehicle. In the proposed framework, a sparse feature points matching and a dense stereo matching are performed in parallel for each stereo image pair. The former process is used to analyze motions of the vehicle itself and also surrounding moving objects. The latter process calculates dense disparity image, as well as U-V disparity maps applied for pixel-wise moving objects segmentation and dynamic occupancy grid mapping. Principal advantage of the proposed framework is the ability of mapping occupied areas and moving objects at the same time. Meanwhile, compared with some existing methods, the stereo-vision based occupancy grid mapping algorithm is improved. The proposed method is verified in real datasets acquired by our platform SeT-Car.

Hybrid Metric-Topological 3D Occupancy Grid Maps for Large-scale Mapping