Ahad Harati

Orthogonal SLAM: a Step toward Lightweight Indoor Autonomous Navigation

Today, lightweight SLAM algorithms are needed in many embedded robotic systems. In this paper the orthogonal SLAM (OrthoSLAM ) algorithm is presented and empirically validated. The algorithm has constant time complexity in the state estimation and is capable to run real-time. The main contribution resides in the idea of reducing the complexity by means of an assumption on the environment. This is done by mapping only lines that are parallel or perpendicular to each other which represent the main structure of most indoor environments. The combination of this assumption with a Kalman filter and a relative map approach is able to map our laboratory hallway with the size of 80 m times 50 m and a trajectory of more than 500 m. The precision of the resulting map is similar to the measurements done by hand which are used as the ground-truth

A lightweight SLAM algorithm using Orthogonal planes for indoor mobile robotics

Simple, fast and lightweight SLAM algorithms are necessary in many embedded robotic systems which soon will be used in houses and offices in order to do various service tasks. In this paper the Orthogonal SLAM algorithm is presented as an answer to this need. In continuation of our previous work, the algorithm is extended to generate 3D maps and empirically validated by mapping the long corridor of our lab with the accuracy comparable with hand measured ground truth. The main contribution resides in the idea of reducing the complexity by using orthogonality constraint in indoor environments. This is done by mapping only planes that are parallel or perpendicular to each other which represent the main structure of most indoor environments. Having this assumption, we use an inclined sensor setup (fixed 2D SICK laser range finders) to generate 3D orthogonal maps. The algorithm is extremely fast since in each step it just processes one line of laser measurements.

Fast range image segmentation for indoor 3D-slam

Abstract Real-time 3D localization and mapping is eventually needed in many service robotic applications. Toward a light and practical SLAM algorithm, we focus on feature extraction via segmentation of range images. Using horizontal and vertical traces of the range matrix, 2D observed polygons are considered for calculation of a one-dimensional measure of direction, called Bearing Angle (BA). BA is the incident angle between the laser beam and edges of the observed polygon by the scanner in the selected direction. Based on this measure, two different approaches to range image segmentation, region- and edge-based, are proposed and evaluated through a set of standard analysis. It is experimentally shown that for navigation applications, edge based approaches are more efficient. Extensive tests on real robots prove BA-based segmentation is successful for SLAM.

Incremental object part detection toward object classification in a sequence of noisy range images

This paper presents an incremental object part detection algorithm using a particle filter. The method infers object parts from 3D data acquired with a range camera. The range information is quantized and enhanced by local structure to partially cope with considerable measurement noise and distortion. The augmented voxel representation allows the adaptation of known track-before-detect algorithms to infer multiple object parts in a range image sequence even when each single observation does not contain enough information to do the detection. The appropriateness of the method is successfully demonstrated by two experiments for chair legs.

Object classification based on a geometric grammar with a range camera

This paper proposes an object classification framework based on a geometric grammar aimed for mobile robotic applications. The paper first discusses the geometric grammar as a compact representation form for object categories with primitive parts as its constituent elements. The paper then discusses the object classification implemented as parsing of primitive parts. In particular, two approaches are discussed that constrain the search space in order to render the parsing of the primitive parts practical. The two approaches are experimentally verified, first, for a generic object category of chair applied to real range images acquired with a range camera mounted on a mobile robot and, second, for multiple generic object categories applied to synthetic range images. The experimental results show the practicability of the framework.

A hierarchical concept oriented representation for spatial cognition in mobile robots

Robots are rapidly evolving from factory work-horses to robot-companions. The future of robots, as our companions, is highly dependent on their abilities to understand, interpret and represent the environment in an efficient and consistent fashion, in a way that is compatible to humans. The work presented here is oriented in this direction. It suggests a hierarchical, concept oriented, probabilistic representation of space for mobile robots. A salient aspect of the proposed approach is that it is holistic - it attempts to create a consistent link from the sensory information the robot acquires to the human-compatible spatial concepts that the robot subsequently forms, while taking into account both uncertainty and incompleteness of perceived information. The approach is aimed at increasing spatial awareness in robots.