Automation driving techniques have seen tremendous progresses these last years, particularly due to a better perception of the environment. In order to provide safe yet not too conservative driving in complex urban environment, data fusion should not only consider redundant sensing to characterize the surrounding obstacles, but also be able to describe the uncertainties and errors beyond presence absence (be it binary or probabilistic). This paper introduces an enriched representation of the world, more precisely of the potential existence of obstacles through an evidential grid map. A method to create this representation from 2 very different sensors, laser scanner and stereo camera, is presented along with algorithms for data fusion and temporal updates. This work allows a better handling of the dynamic aspects of the urban environment and a proper management of errors in order to create a more reliable map. We use the evidential framework based on the Dempster-Shafer theory to model the environment perception by the sensors. A new combination operator is proposed to merge the different sensor grids considering their distinct uncertainties. In addition, we introduce a new long-life layer with high level states that allows the maintenance of a global map of the entire vehicle's trajectory and distinguish between static and dynamic obstacles. Results on a real road dataset show that the environment mapping data can be improved by adding relevant information that could be missed without the proposed approach.
Recent work shows the potential use of evidential grid maps for different tasks than only mapping, such as tracking of obstacles @cite_7 @cite_1 and trajectory planning @cite_5 @cite_25 . To perform these tasks in a safe matter for an intelligent vehicle, the fusion of several perception sensors is essential. There are some studies in the literature where a 2D laser scanner and a camera are used together to perform navigation tasks. @cite_4 proposed an approach for SLAM using a 2D laser scanner and 3D information from a stereo camera for indoor environments based on the Bayesian occupancy grids. In a similar way, @cite_2 explore the fusion of stereo camera and 2D laser scanner in Bayesian occupancy grids to perform path planning. In order to better handle the uncertainties of the sensors, this papers presents a powerful framework based on evidential maps and fusion between such maps that provides more information about object presence and hence allows to better distinguish between static and mobile obstacles. To our knowledge, this is the first work presenting data fusion with different sensors in evidential maps and it shows it can be further developed with more than 2 sensors.
@cite_4 @cite_7 @cite_1 @cite_2 @cite_5 @cite_25
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2D laser scanners have been widely used for accomplishing a number of challenging AI and robotics tasks such as mapping of large environments and localization in highly dynamic environments. However, using only one 2D laser scanner could be insufficient and less reliable for accomplishing tasks in 3D environments. The problem could be solved using multiple 2D laser scanners or a 3D laser scanner for performing 3D perception. Unfortunately, the cost of such 3D sensing systems is still too high for enabling AI and robotics applications. In this paper, we propose to use a 2D laser scanner and a stereo camera for accomplishing simultaneous localization and mapping (SLAM) in 3D indoor environments in which the 2D laser scanner is used for SLAM and the stereo camera is used for 3D mapping. The experimental results demonstrate that the proposed system is lower cost yet effective, and the obstacle detection rate is significant improved compares to using one 2D laser scanner for mapping. Tracking and mapping the local environment form the basis of an autonomous vehicle system. They are often realized separately using occupancy grids, which do not require object or shape assumptions, and model-based object tracking algorithms. Many approaches require a binary classification of the sensor measurements into coming from a static or from a dynamic object, as otherwise inconsistencies between the different representations are likely to occur. This paper presents grid-based tracking and mapping (GTAM), a low-level grid-based approach that simultaneously estimates the static and the dynamic environment, their uncertainties, velocities, as well as information about free space. GTAM works on the level of grid cells, rather than creating object hypotheses. A particle filter is used to obtain continuous cell velocity distributions for all obstacles. Continuous evidences in a Dempster–Shafer model are derived without requiring a binary pre-classification of the sensor measurements. Results and evaluations using a vehicle moving in real dynamic street environments demonstrate the performance of the presented approach. Occupancy grid mapping approaches, especially those that additionally estimate the dynamics, enable a robust and consistent modeling of the local environment in a cell-level representation. But a scene understanding of surrounding traffic participants requires a generalized object-level representation. This work presents an object tracking approach based on dynamic occupancy grids. The association of occupied grid cells with existing object tracks is solved individually on the cell-level without clustering or forming object hypotheses. New object tracks are extracted using a clustering strategy and a velocity variance analysis of neighboring occupied cells to reduce false positives. In order to improve the estimates of the position and size, an object boundary extraction is presented that takes the surrounding free space of the selected box representation into account. Experimental results with real sensor data show the effectiveness of the proposed object tracking approach in challenging urban scenarios with dense traffic. 2D laser range finders have been widely used in mobile robot navigation. However, their use is limited to simple environments containing objects of regular geometry and shapes. Stereo vision, instead, provides 3D structural data of complex objects. In this paper, measurements from a stereo vision camera system and a 2D laser range finder are fused to dynamically plan and navigate a mobile robot in cluttered and complex environments. A robust estimator is used to detect obstacles and ground plane in 3D world model in front of the robot based on disparity information from stereo vision system. Based on this 3D world model, 2D cost map is generated. A separate 2D cost map is also generated by 2D laser range finder. Then we use a grid-based occupancy map approach to fuse the complementary information provided by the 2D laser range finder and stereo vision system. Since the two sensors may detect different parts of an object, two different fusion strategies are addressed here. The final occupancy grid map is simultaneously used for obstacle avoidance and path planning. Experimental results obtained form a Point Grey's Bumblebee stereo camera and a SICK LDOEM laser range finder mounted on a Packbot robot are provided to demonstrate the effectiveness of the proposed lidar and stereo vision fusion strategy for mobile robot navigation. The uncertainty in environment perception is one of the challenges that we face in trajectory planning. For autonomous vehicle to be efficient, they need to be able to deal with this kind of uncertainty. In this work, we combine two existing frameworks: the Belief Functions to build evidential occupancy grid and clothoid tentacles for trajectory planning. First, we use evidential grids to represent the environment and the uncertainties which arise from ignorance and errors during the perception process. Secondly, we generate a set of clothoid tentacles in the egocentered reference frame related to the ego-vehicle, those tentacles represent possible local trajectories. Thirdly, we modify the evidential grid in order to take into consideration some traffic rules such as safety distance between vehicles. Then to choose the best tentacle to execute, we use reward system of a Markov Decision Process-like model to evaluate generated tentacles regarding several criteria including uncertainty represented by the evidential grid. Real and simulated data were used to validate the planning algorithm with evidential grids. Probability theory has become the standard framework in the field of mobile robotics because of the inherent uncertainty associated with sensing and acting. In this paper, we show that the theory of belief functions with its ability to distinguish between different types of uncertainty is able to provide significant advantages over probabilistic approaches in the context of robotics. We do so by presenting solutions to the essential problems of simultaneous localization and mapping (SLAM) and planning based on belief functions. For SLAM, we show how the joint belief function over the map and the robot's poses can be factored and efficiently approximated using a Rao-Blackwellized particle filter, resulting in a generalization of the popular probabilistic FastSLAM algorithm. Our SLAM algorithm produces occupancy grid maps where belief functions explicitly represent additional information about missing and conflicting measurements compared to probabilistic grid maps. The basis for this SLAM algorithm are forward and inverse sensor models, and we present general evidential models for range sensors like sonar and laser scanners. Using the generated evidential grid maps, we show how optimal decisions can be made for path planning and active exploration. To demonstrate the effectiveness of our evidential approach, we apply it to two real-world datasets where a mobile robot has to explore unknown environments and solve different planning problems. Finally, we provide a quantitative evaluation and show that the evidential approach outperforms a probabilistic one both in terms of map quality and navigation performance. A belief-function-based approach to SLAM for mobile robots is presented.Different types of uncertainty are explicitly represented in evidential grid maps.Optimal navigation and exploration based on evidential grid maps is shown.Evidential forward and inverse models for range sensors are provided.The approach is evaluated using real-world datasets recorded by a mobile robot.