Aniket Murarka

Detecting obstacles and drop-offs using stereo and motion cues for safe local motion

A mobile robot operating in an urban environment has to navigate around obstacles and hazards. Though a significant amount of work has been done on detecting obstacles, not much attention has been given to the detection of drop-offs, e.g., sidewalk curbs, downward stairs, and other hazards where an error could lead to disastrous consequences. In this paper, we propose algorithms for detecting both obstacles and drop-offs (also called negative obstacles) in an urban setting using stereo vision and motion cues. We propose a global color segmentation stereo method and compare its performance at detecting hazards against prior work using a local correlation stereo method. Furthermore, we introduce a novel drop-off detection scheme based on visual motion cues that adds to the performance of the stereo-vision methods. All algorithms are implemented and evaluated on data obtained by driving a mobile robot in urban environments.

A stereo vision based mapping algorithm for detecting inclines, drop-offs, and obstacles for safe local navigation

Mobile robots have to detect and handle a variety of potential hazards to navigate autonomously. We present a real-time stereo vision based mapping algorithm for identifying and modeling various hazards in urban environments - we focus on inclines, drop-offs, and obstacles. In our algorithm, stereo range data is used to construct a 3D model consisting of a point cloud with a 3D grid overlaid on top. A novel plane fitting algorithm is then used to segment the 3D model into distinct potentially traversable ground regions and fit planes to the regions. The planes and segments are analyzed to identify safe and unsafe regions and the information is captured in an annotated 2D grid map called a local safety map. The safety map can be used by wheeled mobile robots for planning safe paths in their local surroundings. We evaluate our algorithm comprehensively by testing it in varied environments and comparing the results to ground truth data.

Building Local Safety Maps for a Wheelchair Robot using Vision and Lasers

To be useful as a mobility assistant for a human driver, an intelligent robotic wheelchair must be able to distinguish between safe and hazardous regions in its immediate environment. We present a hybrid method using laser rangefinders and vision for building local 2D metrical maps that incorporate safety information (called local safety maps). Laser range-finders are used for localization and mapping of obstacles in the 2D laser plane, and vision is used for detection of hazards and other obstacles in 3D space. The hazards and obstacles identified by vision are projected into the travel plane of the robot and combined with the laser map to construct the local 2D safety map. The main contributions of this work are (i) the definition of a local 2D safety map, (ii) a hybrid method for building the safety map, and (iii) a method for removing noise from dense stereo data using motion.

Adapting proposal distributions for accurate, efficient mobile robot localization

When performing probabilistic localization using a particle filter, a robot must have a good proposal distribution in which to distribute its particles. Once weighted by their normalized likelihood scores, these particles estimate a posterior distribution over the possible poses of the robot. This paper 1) introduces a new action model (the system of equations used to determine the proposal distribution at each time step) that can run on any differential drive robot, even from log file data, 2) investigates the results of different algorithms that modify the proposal distribution at each time step in order to obtain more accurate localization, 3) investigates the results of incrementally adapting the action model parameters based on recent localization results in order to obtain proposal distributions that better approximate the true posteriors. The results show that by adapting the action model over time and, when necessary, modifying the resulting proposal distributions at each time step, localization improves-the maximum likelihood score increases and, when possible, the percentage of wasted particles decreases

3D pose estimation for planes

This paper presents a method to robustly track planes and estimate their 3D poses in a video. A weighted incremental normal estimation method for planes (WINEP) is presented using Bayesian inference. This estimation method guarantees an optimal solution based on all the observations up to the current time, and the computational cost at each time step does not increase with the growing number of past frames. The tracking algorithm integrates boundary information with point feature tracking, which avoids accumulating errors due to intensity changes, image noise, and inaccurate parameter estimation. The tracking algorithm deals with low-textured as well as highly-textured planes. The tracked boundary locations provide the input data for 3D plane pose estimation. Experiments show that our hybrid tracking method using both point and line features is better than using only point features, and our pose estimation algorithm is more robust and accurate than the conventional homography decomposition method, especially under circumstances of noisy observations and low number of input features.

Using CAD drawings for robot navigation

Exploration and navigation of an environment by a robot usually involves the steps of mapping, localization and path planning. Here we look at the problem of navigation in an environment about which the robot has some a priori information available, namely in the form of an architectural CAD drawing. The CAD drawing is utilized to obtain: (i) a topological map of the environment which is used for large scale path planning between regions in the environment and (ii) a skeleton of each region in the environment for path planning within regions. We then propose that a hierarchy of representations consisting of the topological map, skeleton and a reactive hazard-avoidance control system can be used effectively for navigation and exploration by a robot.

Toward autonomous scientific exploration of ice-covered lakes—Field experiments with the ENDURANCE AUV in an Antarctic Dry Valley

Chemical properties of lake water can provide valuable insight into its ecology. Lakes that are permanently frozen over with ice are generally inaccessible to comprehensive exploration by humans. This paper describes the integration of several novel and existing technologies into an autonomous underwater robot, ENDURANCE, that was successfully used for gathering scientific data in West Lake Bonney in Taylor Valley, Antarctica, in December 2008. This paper focuses on three novel technological and algorithmic solutions. First, a robust position estimation system that uses an acoustic beacon to complement traditional dead-reckoning is described. Second, a novel vision-based docking algorithm for locating and ascending a vertical shaft by tracking a blinking light source is presented. Third, a novel profiling system for measuring water properties while causing minimal water disturbance is described. Finally, experimental results from the scientific missions in 2008 in West Lake Bonney are presented.