CORAL Group - Carnegie Mellon University

CoBot Robots

Indoor Mobile Robot Localization

Our CoBot robots are capable of autonomous localization and navigation in our Gates-Hillman Center using WiFi, LIDAR, and/or the Kinect.

Corrective Gradient Refinement for indoor mobile robot localization

Source code is available at:
The Corrective Gradient Refinement (CGR) algorithm for Monte Carlo Localization (MCL) uses the state space gradients of the observation model to improve accuracy while maintaining low computational requirements. See:

Fast Sampling Plane Filtering of Depth Images

Source code is available at:
Fast Sampling Plane Filtering (FSPF) is a RANSAC based algorithm for extracting 3D points corresponding to planar features, given a depth image. The plane filtered points may be used for localization, or to build polygon maps of environments. See:

The video below demonstrates real-time plane filtering, polygonalization and polygon merging for a scene observed using the Kinect sensor.

Kinect Localization

Cobot uses its Kinect sensor for localization as well as for obstacle avoidance. The Kinect localization algorithm is based on CGR, and runs in real time at full frame rates and at full resolution (640x480 @30fps) while consuming <20% CPU on a single core of the Intel Core i5 540M (2.53GHz) processor. The mean localization error of the robot over experiment trials (of length >4km) while using the Kinect for localization is <20cm and <0.5°. See: Depth Camera based Localization and Navigation for Indoor Mobile Robots, Joydeep Biswas and Manuela Veloso, presented at the RGB-D Workshop at RSS 2011. The video below demonstrates real-time localization using the Kinect sensor on Cobot.

Wifi localization and navigation for autonomous indoor mobile robots

We developed a WiFi based localization system which localized cobot on a graph based map including WiFi data (Means and Standard Deviations) at every vertex of the graph. Cobot 1 succesfully localized and navigated autonomously along this graph based map. For a detailed description of the WiFi based localization algorithm, see: