This chapter proposes an approach which is capable of modeling the environment using a variable resolution grid. The variable resolution grid is stored in a hierarchy of axis-aligned rectangular cuboids, which is generated incrementally and adapted based on sensor observations. In addition, the presented approach is quite flexible as it allows the user to define the maximum number of entries per node thereby influencing its performance in terms of the number of nodes required in the hierarchy for representation as well as the insertion and access times. An extensive evaluation is carried out of the proposed approach in comparison to the state-of-the-art Octomap approach on a publicly available dataset.
The evaluation shows that the proposed approach requires less number of grid cells to approximate the environment and furthermore allows faster access times of the occupied regions in the grid.
Future work includes an evaluation of the proposed approach in modeling dynamic environments. The scope of this thesis has been limited to static environments, however the proposed fusion process is easily extendable to environments containing dynamics.
This extension is possible by splitting the fused cells of the dynamic region based on the chosen resolution of the grid, if the occupancy probability goes above or below the clamping threshold. Additionally, future work also includes an evaluation of different search strategies for the grid cell fusion process of the Rtree based adaptive occupancy grid.
3 Laser Intensities for SLAM
Summary and Contribution: This chapter contributes in the domain of SLAM by proposing an approach that is capable of acquiring surface reflectivity characteristics from laser scanner observations for robot pose estimation and mapping. Hence this chapter discusses a simple calibra-tion approach to acquire a pose-invariant measure of surface reflectivity from laser scanner observations. Furthermore, this reflectivity measure is embedded in an extension of the Hector SLAM algorithm which utilizes this information for pose estimation as well as acquiring a reflectivity map of the environment i.e. occupancy grid map augmented with surface reflectivity characteristics. An extensive experimental evaluation is car-ried out to highlight the advantages as well as attributes of the calibration approach and the proposed extension of the Hector SLAM algorithm.
3.1 Introduction
The research work in the field of Simultaneous Localization and Mapping (SLAM) [59, 82,90] has provided robots the capability of simultaneously estimating their own pose and acquiring an accurate topological/metric map of the environment. The previous chapter of this thesis focused on the aspect of environment representation, which provides the foundation for creating a map by defining the geometric primitive used for approximating the environment. In contrast this chapter focuses on SLAM, which couples the geometric primitive used for environment representation with the robot pose estimation process to allow online, incremental map generation of the environment based on sensor observations.
An accurate map of the environment is essential requirement for a variety of robotic tasks such as global localization, navigation and exploration. SLAM has been an active research area in the field of robotics due to its application in the domain of autonomous driving, personal assistive robots etc. The SLAM algorithm consists of two core components:
firstly the pose estimation and secondly the map creation process. A good pose estimate is required to generate an accurate map and at the same time an accurate map is essential for accurate pose estimation, hence SLAM is typically titled thechicken-and-egg problem.
The initial research focus of the robotics community within the domain of SLAM was on the development of filtering algorithms such as the extended Kalman filter (EKF) [39,171].
The research community has focused on different aspects of EKF SLAM i.e. computational complexity [39, 151] as well as the consistency of the algorithm [4, 80]. The complexity of the EKF isO(n2) due to the covariance matrix update wherenis the number of landmarks in the map. The EKF has been successfully applied for small scale environments, however the quadratic complexity limits its usage for environments containing a large number of features. To deal with this complexity different approaches have been proposed that rely on
map update in local regions [62, 180]. Recently, the extended information filter [185, 194]
has been proposed which takes advantage of the sparseness of the information matrix (i.e.
inverse of the covariance matrix) to deal with the above mentioned issue. In addition, a divide and conquer mechanism [151] been proposed that has linear complexity in the number of landmarks in the map. The research community has also focused on the aspect of inaccuracies caused by the linearization of the EKF leading to the usage of the unscented Kalman filter (UKF) [112]. Another aspect of intense focus within the SLAM community has been to relax the Gaussian assumption associated with Kalman filters, as the mobile robot kinematics are nonlinear leading to non Gaussian distributions. To resolve this issue, particle filter [59, 122, 123] based approaches have been proposed that try to explicitly model the distribution using samples. The interest in the application of particle filters for SLAM has mainly been driven by the increase in computational power in the last few decades.
In contrast to the usage of filtering algorithms i.e. EKF, UKF or the particle filters, recently the research work in the robotics community has focused on the usage of smoothing algorithms for SLAM [36, 81, 82]. Along similar lines, different graph optimization based SLAM approaches have also been proposed [95,186]. The majority of this work is inspired by the research on (sparse) bundle adjustment in the domain of computer vision and photogrammetry [66, 93, 105, 192]. In graph SLAM literature, the entire framework is typically divided into two components: the front-end and the back-end. The front-end deals with the raw sensor data and generates the graph structure by defining the node positions as well as edge constraints between nodes. These edge constraints can define two different cases: firstly the motion between consecutive robot poses and secondly the case when the robot returns to a previously visited location (loop closure constraints). The back-end takes these constraints and estimates the posterior distribution over the robot poses. In addition in context of SLAM, there also exist scan matching based approaches [37, 119, 143, 145], which can be sufficiently accurate for small scale mapping. Typical examples of such scan matching algorithms include iterative closest point (ICP) [157, 166], Normal distribution transform (NDT) [10, 109] as well as Hector SLAM [90]. These approaches typically estimate the transformation between consecutive robot poses either by simple scan to scan or scan to map matching technique.
The majority of the research work in the domain of SLAM focuses on using laser scanner observations to generate an accurate geometric model of the environment. In addition to measuring the distance to an object, a typical laser scanner also quantifies the remission values, i.e. received optical power, after reflection from the surface. This remission value is termed asintensity and depends (among other parameters) on an intrinsic surface property (surface reflectivity) as well as extrinsic parameters such as distance to the surface and angle of incidence with respect to the surface normal. Hence theoretically speaking given a model that defines the influence of the extrinsic parameters, it is possible to acquire a pose-invariant measure of surface reflectivity which can serve as additional information in a wide variety of robotic applications. This chapter presents a simple data-driven model of laser intensities through which a pose-invariant measure of surface reflectivity can be acquired.
In addition, this measure is used in an extension of the Hector SLAM [90] algorithm which employs it for robot pose estimation and furthermore augments geometric models of the