Comparison of Power Consumption of Modern SLAM Methods on Various Datasets

In autonomous robots, the hardware and the algorithms are limited by the power constraints of the robots. Increasing algorithm efficiency can be considered as a way to deal with power constraints. Considering the processing load of Visual SLAM methods, which are frequently used in autonomous robots and become increasingly widespread, there is a need to investigate the effects of methods on power consumption. In this study, a performance comparison of ORB-SLAM2, which is a feature-based Visual SLAM method, DSO, and LDSO, which are direct Visual SLAM methods was conducted. We perform the comparison on ICL-NUIM, KITTI, and EUROC datasets using NVIDIA Jetson TX1 hardware. Central Processor Unit (CPU) usage, Graphics Processor Unit (GPU) power consumption, and total power consumption were taken into account since hardware and power constraints are very important issue for autonomous systems. Results showed that ORB-SLAM2 is the most efficient method for GPU load and power consumption. GPU power consumption and total power consumption are higher than expected for the DSO method compared to CPU load. The computational cost of Loop-Closure is seen clearly when comparing the DSO and LDSO methods.