8 accepted papers
The ability to process environment maps across multiple sessions is critical for robots operating over extended periods of time. Specifically, it is desirable for autonomous agents to detect changes amongst maps of different sessions so as to gain a conflict-free understanding of the current environ
We propose VLASE, a framework to use semantic edge features from images to achieve on-road localization. Semantic edge features denote edge contours that separate pairs of distinct objects such as building-sky, road-sidewalk, and building-ground. While prior work has shown promising results by utili…
RGBD SLAM systems have shown impressive results, but the limited field of view (FOV) and depth range of typical RGBD cameras still cause problems for registering distant frames. Monocular SLAM systems, in contrast, can exploit wide-angle cameras and do not have the depth range limitation, but are un…
We present a new image feature detection method. Our method selects features based on segmenting points with high local intensity variations across different scales using a robust rank order statistics approach. Our method produces a large number of repeatable features that are invariant to several…
Range images captured by Time-of-Flight (ToF) cameras are corrupted with multipath distortions due to interaction between modulated light signals and scenes. The interaction is often complicated, which makes a model-based solution elusive. We propose a learning-based approach for removing the multip
We present an object detection and tracking framework integrated into a simultaneous localization and mapping (SLAM) system using an RGB-D camera. We propose a compact representation of objects by grouping features hierarchically. Similar to a keyframe being a collection of features, an object is re…
Binary descriptors allow fast detection and matching algorithms in computer vision problems. Though binary descriptors can be computed at almost two orders of magnitude faster than traditional gradient based descriptors, they suffer from poor matching accuracy in challenging conditions. In this pape…
Conventional SLAM systems with an RGB-D sensor use depth measurements only in a limited depth range due to hardware limitation and noise of the sensor, ignoring regions that are too far or too close from the sensor. Such systems introduce registration errors especially in scenes with large depth var