Simultaneous location and mapping (SLAM) is a necessary but difficult part of autonomous operation in a forest environment. Forest robots take sharp turns and face dramatic alterations in spatial orientation. Tree stems, foliage, and ground growth are widely variable and demanding objects for keypoint algorithms. We propose an alternative sparse SLAM pipeline, which consists of preliminary environment categorization, frame acceptance tests, keypoint registration, compressed environment sensing, and ends to location registration and SLAM with a constant quality criteria, which substitute closed-loop checks. These point cloud (PC) processing steps are presented in order to cover needs of many possible forest robot implementations and missions in a Boreal mature pine environment. We propose multiple quality criteria along the pipeline steps while assessing existing measures. Mission failure estimates are important for dual use (civilian and military) scenarios. We review SLAM pipelines suitable for reduced computational environments and propose a SLAM performance measure and a mission success measure for a case of autonomous forest mapping using a ground laser scanner and autonomous map utilization. The data of a test case and the comparison of sparse and dense SLAM with an equal PC merge error of 0.18...0.21 m are given.