This section presents the main conclusions of the proposed binary vocabulary generation mechanism as well as highlights the advantages and characteristics of the place recognition pipeline based on laser intensities.
4.8.1 Binary Bag of Words Vocabulary Generation for Loop Closure Detection
A subsection of this chapter focused on an online, incremental approach of binary visual vocabulary generation for loop closure detection. The main purpose of focusing on binary descriptor based vocabularies is because they require reduced computational and memory complexity in comparison to real valued descriptors. The proposed binary vocabulary gen-eration process is based on tracking features across consecutive images making it invariant to the robot pose and ideal for detecting loop closures. In addition, a simple mechanism for generating and updating the binary vocabulary is presented which is coupled with a similarity function and temporal consistency constraints to generate loop closure candi-dates. The proposed approach is evaluated on different publicly available datasets and it has been shown that in comparison to the state of the art it is capable of generating higher recall at 100% precision.
4.8.2 Place Recognition using Active and Passive Sensors
In addition to the loop closure detection pipeline with temporal constraints, this chapter also addresses the problem of place recognition under challenging lighting conditions using active and passive sensors. A generic pipeline for place recognition is presented which can be adapted for different robotic and computer vision applications depending on the desired set of characteristics i.e. the capability of operating under challenging lighting conditions, requirement of any prior training data, odometery, GPS or any temporal con-sistency contraints over sensor observations. The proposed place recognition pipeline is evaluated on a dataset collected in the city of Munich near the TUM campus in which different locations are visited during the day and later revisited during the night time.
The experimental evaluation shows that using intensity images as input in comparison to types of input data, such as camera or range images, is beneficial for the place recognition algorithms (operating with local or global descriptors) operating under challenging lighting conditions. In addition, it shows that given the same place recognition pipeline (based on local or global descriptors given the same parameter settings), intensities generate better precision-recall curves in comparison to other types of input data. The results also under-line the importance of using intensity textured point clouds for 3D point cloud based place recognition. The evaluation also highlights certain design decisions in context of place recognition algorithms such as the strong dependence of global descriptors on observer orientation, the effect of the limited field of view of the rectilinear projection model as
4.8 Conclusion
well as the decrease in performance due to downsampling of point clouds. In summary, the proposed pipeline based on laser intensities is capable of generating high precision, recall under adverse lighting conditions on a challenging dataset without any requirement of prior training data, odometry, GPS or any temporal consistency constraints.
This section presents a brief summary, conclusions and possible future research directions in context of the main contributions of this thesis.
5.1 Summary
This thesis contributes in the domain of perception within the field of mobile robotics by proposing techniques that allow robots to generate accurate maps of the environment.
An accurate map is an essential requirement for a wide variety of tasks such as robotic navigation and exploration. This section provides a summary of the main contributions made by this thesis in the areas ofEnvironment representation,Simultaneous Localization and Mapping (SLAM) and Loop closure/place recognition detection for consistent and accurate environment mapping.
5.1.1 Environment representation
This thesis contributes in the domain of grid based environment representation by propos-ing an approach which is capable of approximatpropos-ing the environment uspropos-ing a variable res-olution grid. The proposed approach extends the standard occupancy grid by adding a fusion process based on occupancy probabilities that couples the surface representation, i.e. occupancy probabilities, with the spatial decomposition of the grid thereby generat-ing variable resolution grid based environment representations. Furthermore, the variable resolution grid is stored in a hierarchy of axis aligned rectangular cuboids that is incre-mentally generated and adapted based on sensor observations. The main characteristics of the proposed approach are
• Incremental: Allows incremental generation of the grid and the hierarchy based on sensor observations
• Flexible: Provides the flexibility of selecting the maximum number of children per node
• Multiresolution grid cells: Capable of modeling a variable resolution grid
In summary, the main contributions of this thesis in context of environment represen-tation are as follow
• An approach capable of modeling the environment using a variable resolution grid
• A simplistic fusion process that couples the surface attribute i.e. occupancy prob-ability with the spatial decomposition leading to variable resolution representations of the environment in an online, incremental fashion
5.1 Summary
• An extensive experimental evaluation highlighting the characteristics of the proposed approach on a publicly available dataset
5.1.2 Laser Intensities for SLAM
This thesis contributes in the domain of SLAM by proposing a simple calibration process that allows the robot to acquire a pose-invariant measure of surface reflectivity. A typical laser scanner measures the distance to an object as well as quantifies the received optical power after reflection from the object which is termed as theremission or intensity value.
The important aspect about intensities is that it is dependent on an intrinsic surface prop-erty as well as extrinsic parameters such as distance and angle of incidence. This thesis presents a simple calibration process that allows modeling of the extrinsic parameters to acquire a pose-invariant measure of surface reflectivity. This surface reflectivity measure is furthermore used to simultaneously estimate the robot pose as well as acquire a reflec-tivity map, i.e. occupancy grid augmented with surface reflecreflec-tivity information, of the environment.
In summary, the main contributions of this thesis in the domain of SLAM are
• A simplistic calibration process to model extrinsic parameters for acquiring a pose invariant measure of surface reflectivity for different laser scanners
• An extension of Hector SLAM capable of simultaneously estimating the robot pose as well as generating a reflectivity map of the environment
• An extensive evaluation of the calibration process as well as the Hector SLAM ex-tension
5.1.3 Place recognition/Loop closure detection
This thesis contributes towards two different aspects of the place recognition/loop closure problem. The first aspect is related to an online, incremental binary vocabulary genera-tion mechanism for loop closure detecgenera-tion using passive sensors. The main advantage of generating binary vocabularies are that they are computationally and memory efficient in comparison to vocabularies generated using real valued descriptors. The second aspect focuses on highlighting the advantage of using laser intensities for place recognition under challenging lighting conditions in comparison to other types of input data such as camera images or geometry information from laser scanners. The main advantage of laser inten-sities is that they are invariant to ambient lighting conditions and depend on an intrinsic surface property i.e. surface reflectivity.
In summary, the main contributions of this thesis in the domain of place recognition/loop closure detection are
• An online, incremental approach for binary vocabulary generation for loop closure detection
• To highlight the advantage of using laser intensities for place recognition under chal-lenging lighting conditions in contrast to other types of input data such as camera images or geometry information from laser scanners
• An extensive evaluation of the vocabulary generation mechanism and the character-istics of laser intensities using different descriptors, projection models and similarity functions