SLAMcore added dense 2.5D mapping capabilities to its latest software release. Robots require maps of their surroundings and the objects within it to navigate effectively – these maps can include point clouds, 2D flat maps, 2.5D dense maps (with heights), 3D maps and semantic maps that identify objects.
One of the main challenges of robot mapping is that it requires significant compute power, processing time and memory to create maps – especially 2.5D and 3D.
SLAMcore’s software uses stereo cameras and inertial sensors to build 2.5D height maps in real-time. The occupied space is represented as a series of columns of different heights that show what space is filled. Using these rich 2.5D maps, robots and autonomous devices know where objects are and can safely plot routes through real-world environments.
SLAMcore software is designed to run fast prototypes out-of-the box with Intel RealSense depth cameras (D435i or D455) and is optimized for x86 and NVIDIA Jetson processors. The software can be further customized for production systems to run on a wide range of cost-effective hardware from Raspberry Pi to GPU-based systems. SLAMcore software fuses visual, inertial (IMU) and depth sensor feeds for high levels of accuracy, speed and efficiency.
“Sparse maps are essential to position and locate robots, but provide little additional information,” said SLAMcore founder and CEO Owen Nicholson. “Using visual inertial SLAM to create richer 2.5D maps lets software developers build much better navigation systems without investing huge amounts of time and resource on creating custom sensor and SLAM code. Our software, tuned to work perfectly with the leading sensors and processors, immediately delivers SLAM capabilities for a vast range of robot designs and consumer products – letting developers focus on the main function of their robot solutions.”
According to SLAMcore’s website, it’ll soon be releasing SLAMcore Map 3D, which offers real-time 3D dense reconstruction.
SLAMcore’s newest software also features expanded integration with the Robot Operating System (ROS). Among the new features is the ability to fuse data from wheel odometry sensors into the SLAMcore algorithms via ROS. Support for ROS-compliant wheel odometry sensors not only increases the reliability and accuracy of mapping and location, but opens the door to a wide range of sensor inputs via ROS interfaces.