9 accepted papers
We present an approach for radar-inertial odometry which uses a continuous-time framework to fuse measurements from multiple automotive radars and an inertial measurement unit (IMU). Adverse weather conditions do not have a significant impact on the operating performance of radar sensors unlike that…
Many self-driving vehicles use a multi-sensor system comprising multiple 3D LiDAR and radar sensors for robust all-round perception. Precise calibration of this multi-sensor system is a critical prerequisite for accurate perception data which facilitates safe operation of self-driving vehicles in hi…
Project AutoVision aims to develop localization and 3D scene perception capabilities for a self-driving vehicle. Such capabilities will enable autonomous navigation in urban and rural environments, in day and night, and with cameras as the only exteroceptive sensors. The sensor suite employs many ca…
We present a real-time dense geometric mapping algorithm for large-scale environments. Unlike existing methods which use pinhole cameras, our implementation is based on fisheye cameras whose large field of view benefits various computer vision applications for self-driving vehicles such as visual-in…
We present a visual odometry (VO) algorithm for a multi-camera system and robust operation in challenging environments. Our algorithm consists of a pose tracker and a local mapper. The tracker estimates the current pose by minimizing photometric errors between the most recent keyframe and the curren…
We present a direct visual odometry algorithm for a fisheye-stereo camera. Our algorithm performs simultaneous camera motion estimation and semi-dense reconstruction. The pipeline consists of two threads: a tracking thread and a mapping thread. In the tracking thread, we estimate the camera pose via…
We present a semi-direct visual odometry algorithm for a fisheye-stereo camera. In a tracking thread, we simultaneously track oriented patches and estimate the camera pose. In a mapping thread, we estimate the coordinates and surface normal for each new patch to be tracked. Estimation of the surface…
In this paper, we propose a novel and computationally efficient algorithm for simultaneous exploration and coverage with a vision-guided micro aerial vehicle (MAV) in unknown environments. This algorithm continually plans a path that allows the MAV to fulfil two objectives at the same time while avo…
Autonomous navigation of micro aerial vehicles (MAVs) in a-priori unknown environments is one of the most challenging problems in robotics. First, a MAV has to incrementally build a 3D geometric map from raw sensor data. Then, based on the mapping information, the path planner has to search for a co…